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497 lines
18 KiB
Python
497 lines
18 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import random
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import numpy as np
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import paddle
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from paddle.nn.quant import weight_quantize
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from fastdeploy.model_executor.ops.gpu import (
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moe_expert_dispatch,
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moe_expert_ffn,
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moe_expert_ffn_wint2,
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moe_expert_reduce,
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)
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def print_tensor_info(t, name):
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if t is not None:
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print(f"-- [print_tensor_info] {name}: shape={t.shape}, dtype={t.dtype}, data_ptr={t.data_ptr():#x}")
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else:
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print(f"-- [print_tensor_info] {name}: tensor is {t}")
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def load_all_tensors(tensor_names, dump_dir):
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tensor_dict = {}
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for name in tensor_names:
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key = name.replace(".pdparams", "").replace("_layer1", "")
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filepath = os.path.join(dump_dir, name)
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if os.path.exists(filepath):
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tensor_dict[key] = paddle.load(filepath)
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if isinstance(tensor_dict[key], paddle.Tensor):
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print_tensor_info(tensor_dict[key], name)
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else:
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print(f"-- {name}: {tensor_dict[key]}")
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else:
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tensor_dict[key] = None
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print(f"-- {name}: {filepath} does not exist.")
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return tensor_dict
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def check_result(dtype, out_1, out_2, check_equal=False):
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def get_flattened_array(out):
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if isinstance(out, paddle.Tensor):
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if out.dtype == paddle.bfloat16:
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res = paddle.cast(out, dtype="float32").numpy()
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else:
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res = out.numpy()
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else:
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res = out
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return res.flatten()
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out_1_flatten = get_flattened_array(out_1)
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out_2_flatten = get_flattened_array(out_2)
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diff = np.abs(out_1_flatten - out_2_flatten)
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max_atol_idx = np.argmax(diff)
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print(
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f"-- max difference : {np.max(diff)}, {out_1_flatten[max_atol_idx]} vs {out_2_flatten[max_atol_idx]}, idx={max_atol_idx}"
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)
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relative_error = np.abs(diff / (out_2_flatten + 1e-8))
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max_rtol_idx = np.nanargmax(relative_error)
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print(
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f"-- max relative error : {np.nanmax(relative_error)}, {out_1_flatten[max_rtol_idx]} vs {out_2_flatten[max_rtol_idx]}"
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)
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if check_equal:
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num_diffs = 0
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for i in range(out_1.size):
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if num_diffs >= 10:
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break
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if out_1_flatten[i] != out_2_flatten[i]:
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print(f"-- {i}: {out_1_flatten[i]} vs {out_2_flatten[i]}")
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num_diffs += 1
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np.testing.assert_array_equal(out_1, out_2)
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else:
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if dtype == "float32":
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if os.getenv("NVIDIA_TF32_OVERRIDE", "1") == "0":
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atol, rtol = 1e-5, 1e-5
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else:
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atol, rtol = 1e-3, 1e-3
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elif dtype == "float16":
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atol, rtol = 1e-3, 1e-3
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elif dtype == "bfloat16":
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atol, rtol = 1e-2, 1e-2
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np.testing.assert_allclose(
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out_1,
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out_2,
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atol=atol,
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rtol=rtol,
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)
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def unzip_and_dequant_wint2(
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w, w_scale, w_code_scale, w_code_zp, w_super_scale=None, scale_compute_dtype=None, shuffled=False, group_size=64
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):
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"""
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w uint8 [num_experts, in_feature_size // pack_num, out_feature_size]
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w_scale [num_experts, in_feature_size // group_size, out_feature_size]
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w_code_scale float32 [num_experts, out_feature_size]
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w_code_zp float32 [num_experts, out_feature_size]
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w_super_scale w_scale.dtype [num_experts, out_feature_size]
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output: w_scale.dtype [num_experts, in_feature_size, out_feature_size]
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"""
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def w_round(x):
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return paddle.floor(x + 0.5)
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# step0: w dtype: uint8, shape: [num_experts, in_feature_size // pack_num, out_feature_size]
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# where pack_num = 4
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pack_num = 4
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bzp = 32
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num_experts, pack_in_feature_size, out_feature_size = w.shape
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in_feature_size = pack_in_feature_size * pack_num
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# step1: w need to unzip to shape: [num_experts, in_feature_size, out_feature_size]
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# here we use broadcast operation to implcitly expand the last dimension
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w = w.transpose(perm=[0, 2, 1]).reshape([num_experts, out_feature_size, pack_in_feature_size, 1])
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# for support repeat_interleave, w cast to int32
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w = w.cast("int32")
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w = w.repeat_interleave(pack_num, axis=-1)
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w = w.reshape([num_experts, out_feature_size, in_feature_size])
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w = w.transpose(perm=[0, 2, 1])
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# step2: w need to first dequant
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# w_code_scale shape: [num_experts, out_feature_size]
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# w_code_zp shape: [num_experts, out_feature_size]
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w_code_scale = w_code_scale.reshape([num_experts, 1, out_feature_size])
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w_code_zp = w_code_zp.reshape([num_experts, 1, out_feature_size])
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w = w_round(w.cast("float32") * w_code_scale + w_code_zp).cast("int32")
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# step3: w need to shifted and mask the original weight to unzip
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bit_shift = paddle.to_tensor([9, 6, 3, 0], dtype="int32")
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in_feature_bit_shift = bit_shift[paddle.arange(in_feature_size) % pack_num]
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in_feature_bit_shift = in_feature_bit_shift.reshape([1, in_feature_size, 1])
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mask = paddle.to_tensor(0x3F, dtype="int32")
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if scale_compute_dtype is None:
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scale_compute_dtype = w_super_scale.dtype if w_super_scale is not None else w_scale.dtype
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group_num = in_feature_size // group_size
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# step4: w_scale need to shift and mask and dequant
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if w_scale.dtype == paddle.uint8:
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# w_scale shape: [num_experts, in_feature_size // group_size, out_feature_size]
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# w_scale packed shape: [num_experts, group_num // 2, out_feature_size]
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w_scale = w_scale.cast("int32")
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w_scale = w_scale.reshape([num_experts, group_num // 2, 1, out_feature_size])
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w_scale = w_scale.repeat_interleave(2, axis=2)
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w_scale = (w_scale >> paddle.to_tensor([4, 0], dtype="int32").reshape([1, 1, 2, 1])) & paddle.to_tensor(
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0xF, dtype="int32"
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)
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w_scale = w_scale.reshape([num_experts, group_num, out_feature_size]).cast(scale_compute_dtype)
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# step5: w need to shift and mask and second dequant
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w = ((w >> in_feature_bit_shift) & mask).cast(w_scale.dtype)
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if w_super_scale is not None:
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# w_super_scale shape: [num_experts, out_feature_size]
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w_super_scale = w_super_scale.reshape([num_experts, 1, out_feature_size])
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w_scale = w_scale * w_super_scale
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# w_scale reshape to [num_experts, in_feature_size, out_feature_size]
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w_scale = w_scale.reshape([num_experts, in_feature_size // group_size, 1, out_feature_size])
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w_scale = w_scale.repeat_interleave(group_size, axis=2).reshape([num_experts, in_feature_size, out_feature_size])
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w = (w - bzp).cast(w_scale.dtype) * w_scale
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if shuffled:
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w = w.reshape([num_experts, in_feature_size // 64, 4, 8, 2, out_feature_size])
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w = paddle.transpose(w, perm=[0, 1, 3, 2, 4, 5])
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w = w.reshape([num_experts, in_feature_size, out_feature_size])
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return w.cast(w_super_scale.dtype)
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class MoEArguments:
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def __init__(
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self,
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quant_method,
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gate_weight,
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ffn1_weight,
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ffn2_weight,
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ffn1_weight_scale,
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ffn2_weight_scale,
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ffn1_local_scale=None,
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ffn1_code_scale=None,
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ffn1_code_zp=None,
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ffn2_local_scale=None,
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ffn2_code_scale=None,
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ffn2_code_zp=None,
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gate_correction_bias=None,
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topk=8,
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):
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self.quant_method = quant_method
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self.gate_weight = gate_weight
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self.gate_correction_bias = gate_correction_bias
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self.topk = topk
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self.ffn1_weight = ffn1_weight
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self.ffn2_weight = ffn2_weight
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self.ffn1_weight_scale = ffn1_weight_scale
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self.ffn2_weight_scale = ffn2_weight_scale
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self.ffn1_local_scale = ffn1_local_scale
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self.ffn1_code_scale = ffn1_code_scale
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self.ffn1_code_zp = ffn1_code_zp
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self.ffn2_local_scale = ffn2_local_scale
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self.ffn2_code_scale = ffn2_code_scale
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self.ffn2_code_zp = ffn2_code_zp
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if quant_method == "none":
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self.dtype = ffn1_weight.dtype
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else:
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self.dtype = ffn1_weight_scale.dtype
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self.num_experts = ffn1_weight.shape[0]
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if ffn1_weight_scale is not None:
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self.intermediate_size = ffn1_weight_scale.shape[1] // 2
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else:
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self.intermediate_size = ffn1_weight.shape[2] // 2
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if ffn2_weight_scale is not None:
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self.hidden_size = ffn2_weight_scale.shape[1]
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else:
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self.hidden_size = ffn2_weight.shape[2]
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def convert_to_bf16(self, shuffled=False):
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if self.quant_method == "weight_only_int2":
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assert (
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self.dtype != self.ffn1_weight.dtype
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), f"dtype:{self.dtype} vs weight_dtype: {self.ffn1_weights.dtype}"
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ffn1_weight = unzip_and_dequant_wint2(
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w=self.ffn1_weight,
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w_scale=self.ffn1_local_scale,
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w_code_scale=self.ffn1_code_scale,
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w_code_zp=self.ffn1_code_zp,
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w_super_scale=self.ffn1_weight_scale,
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shuffled=shuffled,
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group_size=64,
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)
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ffn2_weight = unzip_and_dequant_wint2(
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w=self.ffn2_weight,
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w_scale=self.ffn2_local_scale,
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w_code_scale=self.ffn2_code_scale,
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w_code_zp=self.ffn2_code_zp,
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w_super_scale=self.ffn2_weight_scale,
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shuffled=shuffled,
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group_size=64,
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)
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other = MoEArguments(
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quant_method="none",
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gate_weight=self.gate_weight,
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ffn1_weight=ffn1_weight,
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ffn2_weight=ffn2_weight,
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ffn1_weight_scale=None,
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ffn2_weight_scale=None,
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gate_correction_bias=self.gate_correction_bias,
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topk=self.topk,
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)
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return other
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else:
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assert False, "Not supported now!"
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def convert_to_wint4(self):
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assert self.quant_method == "none"
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assert self.dtype == self.ffn1_weight.dtype, f"dtype:{self.dtype} vs weight_dtype: {self.ffn1_weights.dtype}"
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def quantize_ffn_weight(ffn_weight):
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weight_list = []
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scale_list = []
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for i in range(ffn_weight.shape[0]):
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quant_weight, scale = weight_quantize(ffn_weight[i, :, :], algo="weight_only_int4", arch=80)
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weight_list.append(quant_weight)
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scale_list.append(scale)
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quanted_weight = paddle.stack(weight_list, axis=0)
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scale = paddle.stack(scale_list, axis=0)
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return quanted_weight, scale
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ffn1_weight, ffn1_weight_scale = quantize_ffn_weight(self.ffn1_weight)
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ffn2_weight, ffn2_weight_scale = quantize_ffn_weight(self.ffn2_weight)
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other = MoEArguments(
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quant_method="weight_only_int4",
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gate_weight=self.gate_weight,
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ffn1_weight=ffn1_weight,
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ffn2_weight=ffn2_weight,
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ffn1_weight_scale=ffn1_weight_scale,
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ffn2_weight_scale=ffn2_weight_scale,
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gate_correction_bias=self.gate_correction_bias,
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topk=self.topk,
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)
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return other
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def print(self):
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print("")
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print(f"-- [MoEArguments] dtype: {self.dtype}")
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print(f"-- [MoEArguments] num_experts: {self.num_experts}")
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print(f"-- [MoEArguments] intermediate_size: {self.intermediate_size}")
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print(f"-- [MoEArguments] hidden_size: {self.hidden_size}")
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print_tensor_info(self.gate_correction_bias, "gate_correction_bias")
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print_tensor_info(self.ffn1_weight, "ffn1_weight")
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print_tensor_info(self.ffn2_weight, "ffn2_weight")
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print_tensor_info(self.ffn1_weight_scale, "ffn1_weight_scale")
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print_tensor_info(self.ffn2_weight_scale, "ffn2_weight_scale")
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print_tensor_info(self.ffn1_local_scale, "ffn1_local_scale")
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print_tensor_info(self.ffn2_local_scale, "ffn2_local_scale")
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print_tensor_info(self.ffn1_code_scale, "ffn1_code_scale")
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print_tensor_info(self.ffn2_code_scale, "ffn2_code_scale")
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print_tensor_info(self.ffn1_code_zp, "ffn1_code_zp")
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print_tensor_info(self.ffn2_code_zp, "ffn2_code_zp")
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def prepare_args_wint2(test_dir):
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tensor_names = [
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"x",
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"gate_weight",
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"topk_ids",
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"gate_correction_bias",
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"ffn1_weight",
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"ffn2_weight",
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"ffn1_super_scales",
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"ffn2_super_scales",
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"ffn1_weight_scale",
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"ffn1_code_scale",
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"ffn1_code_zp",
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"ffn2_weight_scale",
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"ffn2_code_scale",
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"ffn2_code_zp",
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]
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tensor_dict = load_all_tensors(tensor_names, test_dir)
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topk = tensor_dict["topk_ids"].shape[1]
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moe_args = MoEArguments(
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quant_method="weight_only_int2",
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gate_weight=tensor_dict["gate_weight"],
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ffn1_weight=tensor_dict["ffn1_weight"],
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ffn2_weight=tensor_dict["ffn2_weight"],
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ffn1_weight_scale=tensor_dict["ffn1_super_scales"],
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ffn2_weight_scale=tensor_dict["ffn2_super_scales"],
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ffn1_local_scale=tensor_dict["ffn1_weight_scale"],
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ffn1_code_scale=tensor_dict["ffn1_code_scale"],
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ffn1_code_zp=tensor_dict["ffn1_code_zp"],
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ffn2_local_scale=tensor_dict["ffn2_weight_scale"],
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ffn2_code_scale=tensor_dict["ffn2_code_scale"],
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ffn2_code_zp=tensor_dict["ffn2_code_zp"],
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gate_correction_bias=tensor_dict["gate_correction_bias"],
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topk=topk,
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)
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return moe_args
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def run_moe_decode_cutlass(moe_args, quant_method, hidden_states, scores):
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# print(f"-- [run_moe_decode_cutlass] {quant_method}")
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def rearrange_weights(w):
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# [num_experts, in_feature_size, out_feature_size]
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w_shape = w.shape
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# [num_experts, in_feature_size / 64, 64, out_feature_size / 8, 8]
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w = w.reshape([w_shape[0], w_shape[1] // 16, 16, w_shape[2] // 8, 8])
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# [num_experts, out_feature_size / 8, in_feature_size // 64, 8, 64]
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w = paddle.transpose(w, perm=[0, 3, 1, 4, 2])
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# w = w.reshape([w_shape[0], w_shape[2] // 8, w_shape[1] // 16, 128])
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w = w.reshape(w_shape)
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return w
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if quant_method == "weight_only_int2":
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ffn1_weight = rearrange_weights(moe_args.ffn1_weight)
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ffn2_weight = rearrange_weights(moe_args.ffn2_weight)
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cache = paddle.empty((int(512e6 // 4),), dtype="int32")
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warmup, repeat = 5, 100
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gpu_timecosts = []
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for i in range(warmup + repeat):
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start_event = paddle.device.Event(enable_timing=True)
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end_event = paddle.device.Event(enable_timing=True)
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cache.zero_() # fast_flush
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start_event.record()
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(
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permute_input,
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token_nums_per_expert,
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permute_indices_per_token,
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topk_weights,
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topk_indices,
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expert_idx_per_token,
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) = moe_expert_dispatch(
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input=hidden_states,
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gating_output=scores,
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gating_correction_bias=moe_args.gate_correction_bias,
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w4a8_in_scale=None,
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moe_topk=moe_args.topk,
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group_moe=False,
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topk_only_mode=moe_args.gate_correction_bias is None,
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)
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if quant_method == "weight_only_int2":
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ffn_out = moe_expert_ffn_wint2(
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permute_input,
|
|
token_nums_per_expert,
|
|
ffn1_weight,
|
|
ffn2_weight,
|
|
None,
|
|
moe_args.ffn1_weight_scale,
|
|
moe_args.ffn2_weight_scale,
|
|
moe_args.ffn1_local_scale,
|
|
moe_args.ffn1_code_scale,
|
|
moe_args.ffn1_code_zp,
|
|
moe_args.ffn2_local_scale,
|
|
moe_args.ffn2_code_scale,
|
|
moe_args.ffn2_code_zp,
|
|
False,
|
|
)
|
|
else:
|
|
ffn_out = moe_expert_ffn(
|
|
permute_input,
|
|
token_nums_per_expert,
|
|
moe_args.ffn1_weight,
|
|
moe_args.ffn2_weight,
|
|
None,
|
|
moe_args.ffn1_weight_scale,
|
|
moe_args.ffn2_weight_scale,
|
|
None,
|
|
None,
|
|
quant_method,
|
|
False,
|
|
)
|
|
|
|
moe_out = moe_expert_reduce(
|
|
ffn_out,
|
|
topk_weights,
|
|
permute_indices_per_token,
|
|
topk_indices,
|
|
None,
|
|
norm_topk_prob=True,
|
|
routed_scaling_factor=1.0,
|
|
)
|
|
end_event.record()
|
|
gpu_timecosts.append(start_event.elapsed_time(end_event))
|
|
cache += int(random.random() * 1000) # change cache
|
|
|
|
paddle.device.synchronize()
|
|
del cache
|
|
gpu_timecosts = gpu_timecosts[warmup:]
|
|
return moe_out, np.quantile(gpu_timecosts, 0.5)
|
|
|
|
|
|
def test_main(test_dir):
|
|
moe_args = prepare_args_wint2(test_dir)
|
|
moe_args.print()
|
|
|
|
quant_method = "weight_only_int2"
|
|
check_acc = False
|
|
|
|
moe_args_bf16 = moe_args.convert_to_bf16(shuffled=True)
|
|
moe_args_wint4 = moe_args_bf16.convert_to_wint4()
|
|
|
|
for num_tokens in [1, 2, 4, 16, 64, 128, 512, 1024]:
|
|
hidden_states = paddle.randn([num_tokens, moe_args.hidden_size]).cast(moe_args.dtype)
|
|
gate_out = paddle.matmul(hidden_states.cast("float32"), moe_args.gate_weight)
|
|
scores = paddle.nn.functional.softmax(gate_out, axis=-1)
|
|
|
|
timecost_wint2, timecost_bf16, timecost_wint4 = 0.0, 0.0, 0.0
|
|
out_wint2, timecost_wint2 = run_moe_decode_cutlass(moe_args, quant_method, hidden_states, scores)
|
|
|
|
out_bf16, timecost_bf16 = run_moe_decode_cutlass(moe_args_bf16, "none", hidden_states, scores)
|
|
out_wint4, timecost_wint4 = run_moe_decode_cutlass(moe_args_wint4, "weight_only_int4", hidden_states, scores)
|
|
|
|
print(
|
|
f"[Time Cost] num_tokens: {num_tokens}, {quant_method}: {timecost_wint2:.5f} ms; bf16: {timecost_bf16:.5f} ms; wint4: {timecost_wint4:0.5f} ms"
|
|
)
|
|
|
|
if check_acc:
|
|
check_result("bfloat16", out_wint2, out_bf16, check_equal=False)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.seed(1024)
|
|
test_dir = os.path.dirname(os.path.abspath(__file__)) + "/ernie45t_tp1_wint2_params"
|
|
test_main(test_dir)
|