init v0.23.0

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

vllm_ascend/quantization/methods/w8a8fp8_dynamic.py

@@ -0,0 +1,102 @@
+#
+# 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.
+#
+
+from typing import Any
+
+import torch
+
+from .base import QuantType
+from .registry import register_scheme
+from .w8a8_dynamic import AscendW8A8DynamicFusedMoEMethod, AscendW8A8DynamicLinearMethod
+
+
+@register_scheme("W8A8FP8_DYNAMIC", "linear")
+class AscendW8A8FP8DynamicLinearMethod(AscendW8A8DynamicLinearMethod):
+    """Linear method for Ascend W8A8FP8_DYNAMIC.
+
+    This scheme uses FP8 dynamic per-token quantization for activations
+    and FP8 per-channel quantization for weights.
+    """
+
+    act_quant_type: torch.dtype = torch.float8_e4m3fn
+
+    def __init__(self):
+        pass
+
+    def get_weight(self, input_size: int, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
+        params_dict = {"weight": torch.empty(output_size, input_size, dtype=torch.float8_e4m3fn)}
+        return params_dict
+
+    def get_perchannel_param(
+        self,
+        output_size: int,
+        params_dtype: torch.dtype,
+    ) -> dict[str, Any]:
+        params_dict = {}
+        params_dict["weight_scale"] = torch.empty(output_size, 1, dtype=torch.float32)
+        params_dict["weight_offset"] = torch.empty(output_size, 1, dtype=params_dtype)
+        return params_dict
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        tp_rank: int | None = 0,
+    ) -> torch.Tensor:
+        output = super().apply(layer, x, bias, tp_rank)
+        # TODO: there is a bug in npu_quant_matmul for fp8 with bias
+        # after the bug is fixed, the whole apply method can be removed.
+        if bias is not None:
+            output = (output + bias).to(x.dtype)
+        return output
+
+
+@register_scheme("W8A8FP8_DYNAMIC", "moe")
+class AscendW8A8FP8DynamicFusedMoEMethod(AscendW8A8DynamicFusedMoEMethod):
+    """FusedMoE method for Ascend W8A8FP8_DYNAMIC."""
+
+    quant_type: QuantType = QuantType.W8A8FP8
+
+    def __init__(self):
+        super().__init__()
+
+    def get_weight(
+        self, num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
+    ) -> dict[str, Any]:
+        param_dict = {}
+        param_dict["w13_weight"] = torch.empty(
+            num_experts, 2 * intermediate_size_per_partition, hidden_sizes, dtype=torch.float8_e4m3fn
+        )
+        param_dict["w2_weight"] = torch.empty(
+            num_experts, hidden_sizes, intermediate_size_per_partition, dtype=torch.float8_e4m3fn
+        )
+        return param_dict
+
+    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 = {}
+        param_dict["w13_weight_scale"] = torch.empty(
+            num_experts, 2 * intermediate_size_per_partition, 1, dtype=torch.float32
+        )
+        param_dict["w13_weight_offset"] = torch.empty(
+            num_experts, 2 * intermediate_size_per_partition, 1, dtype=params_dtype
+        )
+        param_dict["w2_weight_scale"] = torch.empty(num_experts, hidden_sizes, 1, dtype=torch.float32)
+        param_dict["w2_weight_offset"] = torch.empty(num_experts, hidden_sizes, 1, dtype=params_dtype)
+        return param_dict