Sync v2.0 version of code to github repo

fastdeploy/model_executor/layers/quantization/block_wise_fp8.py

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+"""
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 Optional
+
+import paddle
+
+import fastdeploy
+from fastdeploy.model_executor.layers.moe import FusedMoE
+
+from ..utils import per_block_cast_to_fp8, get_tensor
+from .quant_base import QuantConfigBase, QuantMethodBase
+
+
+class BlockWiseFP8Config(QuantConfigBase):
+    """
+    block wise quantization config, only support fp8 quant and only supports loading weights in BF16 format.
+    After loading the weights, it will automatically compute quantization sparsity and dynamically perform
+    per-token quantization of activations during inference.
+    """
+
+    def __init__(self, weight_block_size: list = [-1, -1]) -> None:
+        super().__init__()
+        self.weight_block_size = weight_block_size
+        self.quant_max_bound = 448
+        self.quant_min_bound = -448
+        self.quant_round_type = 1
+
+    def name(self) -> str:
+        return "block_wise_fp8"
+
+    @classmethod
+    def from_config(cls, config: dict) -> "BlockWiseFP8Config":
+        weight_block_size = config.get("weight_block_size", [128, 128])
+        return cls(weight_block_size)
+
+    def get_quant_method(self, layer) -> Optional[QuantMethodBase]:
+        '''
+        Get quantization method.
+        '''
+        if isinstance(layer, FusedMoE):
+            from fastdeploy.model_executor.layers.moe.fused_moe_deepgemm_backend import \
+                DeepGemmFusedMoeMethod
+            return DeepGemmFusedMoeMethod(self)
+        else:
+            return BlockWiseFP8LinearMethod(self)
+
+
+class BlockWiseFP8LinearMethod(QuantMethodBase):
+    """
+    block wise quantization method for linear
+    """
+
+    def __init__(
+        self,
+        quant_config: BlockWiseFP8Config,
+    ) -> None:
+        super().__init__()
+        self.quant_config = quant_config
+
+    def create_weights(self, layer):
+        layer.linear_weight_shape.reverse()
+        layer.linear_weight_scale = layer.create_parameter(
+            shape=[
+                (layer.output_size + self.quant_config.weight_block_size[0] -
+                 1) // self.quant_config.weight_block_size[0],
+                (layer.input_size + self.quant_config.weight_block_size[1] - 1)
+                // self.quant_config.weight_block_size[1],
+            ],
+            dtype="float32",
+            is_bias=False,
+        )
+        layer.weight_dtype = "float8_e4m3fn"
+
+    def process_loaded_weights(self, layer, weights) -> None:
+        weight_tensor = weights.transpose([1, 0])
+        quanted_weight_tensor, weight_block_scale_tensor = (
+            per_block_cast_to_fp8(weight_tensor))
+        layer.linear_weight.copy_(quanted_weight_tensor, False)
+        layer.linear_weight_scale.set_value(weight_block_scale_tensor)
+
+    def process_prequanted_weights(self, layer, state_dict):
+        """
+        process_prequanted_weights
+        """
+        quant_weight = get_tensor(state_dict.pop(layer.weight_key))
+        weight_scale = get_tensor(state_dict.pop(layer.weight_scale_key))
+
+        quant_weight = quant_weight.transpose([1, 0]).contiguous()
+        layer.linear_weight.copy_(quant_weight.view("float8_e4m3fn"), False)
+
+        weight_scale = weight_scale.transpose([1, 0])
+        layer.linear_weight_scale.set_value(weight_scale)
+
+    def apply(self, layer, x):
+        x, x_scale_tensor = fastdeploy.model_executor.ops.gpu.per_token_quant_padding(
+            x, self.quant_config.weight_block_size[0])
+        linear_out = paddle.empty((x.shape[0], layer.output_size),
+                                  dtype=paddle.bfloat16)
+        import fastdeploy.model_executor.ops.gpu.deep_gemm as deep_gemm
+        deep_gemm.gemm_fp8_fp8_bf16_nt(
+            (x, x_scale_tensor),
+            (layer.linear_weight, layer.linear_weight_scale),
+            linear_out,
+        )
+        if layer.with_bias:
+            linear_out = paddle.add(linear_out, layer.linear_bias)
+        return linear_out