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296 lines
10 KiB
Python
296 lines
10 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 numpy as np
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import paddle
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from fastdeploy.model_executor.ops.xpu import moe_expert_ffn
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np.random.seed(2025)
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token_num = 7
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expert_num = 64
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hidden_dim = 8192
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ffn_inter_dim = 7168
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ffn_outer_dim = ffn_inter_dim // 2
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num_max_dispatch_tokens_per_rank = 128
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num_rank = 8
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expert_num_per_rank = expert_num // num_rank
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used_in_ep_low_latency = True
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hadamard_blocksize = 512
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ffn_in = (np.random.random([token_num, hidden_dim]) - 0.5).astype("float32")
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token_num_lod = np.full([expert_num_per_rank + 1], 0, "int32")
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token_num_lod[-1] = token_num
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token_num_lod[1:-1] = np.random.randint(0, token_num, [expert_num_per_rank - 1])
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token_num_lod = np.sort(token_num_lod)
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token_num_per_expert = token_num_lod[1:] - token_num_lod[:-1]
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ffn1_w = (np.random.random([expert_num_per_rank, ffn_inter_dim, hidden_dim]) - 0.5).astype("float32")
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ffn2_w = (np.random.random([expert_num_per_rank, hidden_dim, ffn_outer_dim]) - 0.5).astype("float32")
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ffn2_shift = (np.random.random([1, ffn_outer_dim]) - 0.5).astype("float32")
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ffn2_smooth = (np.random.random([1, ffn_outer_dim]) - 0.5).astype("float32")
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if used_in_ep_low_latency:
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ffn_in_tmp = ffn_in
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ffn_in = np.zeros(
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[
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expert_num_per_rank,
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num_max_dispatch_tokens_per_rank * num_rank,
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hidden_dim,
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],
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"float32",
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)
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for i in range(expert_num_per_rank):
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ffn_in[i][: token_num_per_expert[i]] = ffn_in_tmp[token_num_lod[i] : token_num_lod[i + 1]]
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token_num_info = token_num_per_expert
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else:
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token_num_info = token_num_lod
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print(f"ffn_in: {ffn_in}")
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print(f"token_num_lod: {token_num_lod}")
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print(f"token_num_per_expert: {token_num_per_expert}")
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print(f"ffn1_w: {ffn1_w}")
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print(f"ffn2_w: {ffn2_w}")
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def clip_and_round(x, quant_max_bound=127):
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return np.clip(np.around(x), -quant_max_bound, quant_max_bound).astype("int8")
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def weight_quant_wint8(w_fp32):
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w_max = np.max(np.abs(w_fp32), axis=-1, keepdims=True)
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w_int8 = clip_and_round(w_fp32 / w_max * 127.0)
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return w_int8, w_max.reshape([-1])
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def weight_quant_wint4(w_fp32):
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w_max = np.max(np.abs(w_fp32), axis=-1, keepdims=True)
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w_int4 = clip_and_round(w_fp32 / w_max * 7.0, 7)
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w_int4 = (w_int4[:, :, 1::2] & 0xF) << 4 | (w_int4[:, :, ::2] & 0xF) # pack int4
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return w_int4, w_max.reshape([-1])
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def weight_quant(w_fp32, algo="weight_only_int8"):
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if algo == "weight_only_int8":
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return weight_quant_wint8(w_fp32)
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elif algo == "weight_only_int4":
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return weight_quant_wint4(w_fp32)
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else:
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return None, None
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quant_method = "weight_only_int4"
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print(f"quant_method={quant_method}, used_in_ep_low_latency={used_in_ep_low_latency}")
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ffn1_quant_w, ffn1_w_scale = weight_quant(ffn1_w, quant_method)
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ffn2_quant_w, ffn2_w_scale = weight_quant(ffn2_w, quant_method)
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print(f"ffn1_w {ffn1_w.shape}: {ffn1_w}")
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print(f"ffn2_w {ffn2_w.shape}: {ffn2_w}")
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print(f"ffn1_quant_w {ffn1_quant_w.shape}: {ffn1_quant_w}")
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print(f"ffn1_w_scale {ffn1_w_scale.shape}: {ffn1_w_scale}")
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print(f"ffn2_quant_w {ffn2_quant_w.shape}: {ffn2_quant_w}")
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print(f"ffn2_w_scale {ffn2_w_scale.shape}: {ffn2_w_scale}")
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def weight_dequant_wint8(w_int, w_scale):
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w_shape = w_int.shape
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w_scale_new_shape = list(w_shape)
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w_scale_new_shape[-1] = 1
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w_scale_new = w_scale.reshape(w_scale_new_shape)
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w_fp32 = w_int.astype("float32") / 127.0 * w_scale_new
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return w_fp32
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def weight_dequant_wint4(w_int, w_scale):
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w_shape = w_int.shape
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w_scale_new_shape = list(w_shape)
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w_scale_new_shape[-1] = 1
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# w_scale_new_shape[-2] = w_scale_new_shape[-2] * 2
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w_scale_new = w_scale.reshape(w_scale_new_shape)
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w_new_shape = list(w_shape)
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w_new_shape[-1] = w_new_shape[-1] * 2
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w_int8 = np.zeros(w_new_shape, dtype=np.int8)
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w_int8[:, :, ::2] = w_int & 0xF
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w_int8[:, :, 1::2] = (w_int >> 4) & 0xF
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w_int8 = np.where(w_int8 >= 8, w_int8 - 16, w_int8)
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w_fp32 = w_int8.astype("float32") / 7.0 * w_scale_new
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return w_fp32
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def weight_dequant(w_int, w_scale, algo="weight_only_int8"):
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if algo == "weight_only_int8":
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return weight_dequant_wint8(w_int, w_scale)
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elif algo == "weight_only_int4":
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return weight_dequant_wint4(w_int, w_scale)
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else:
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return None, None
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def fwt(a):
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"""
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快速 Walsh-Hadamard 变换（正向变换）
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:param a: 输入列表，长度必须是2的幂
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:return: 变换后的列表
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"""
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n = len(a)
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# 检查输入长度是否为2的幂
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if n == 0 or n & (n - 1) != 0:
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raise ValueError("输入长度必须是2的幂")
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# 复制输入以避免修改原始数据
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a = a.copy()
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h = 1
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while h < n:
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for i in range(0, n, 2 * h):
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for j in range(i, i + h):
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x = a[j]
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y = a[j + h]
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a[j] = x + y
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a[j + h] = x - y
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h <<= 1 # 等同于 h *= 2
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return a
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def hadamard(_x, block_size):
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x = np.copy(_x).reshape((-1, _x.shape[-1]))
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if block_size == -1:
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return x
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m = 1
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n = x.shape[-1]
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for i in range(len(x.shape) - 1):
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m = m * x.shape[i]
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for i in range(m):
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for j in range(0, n, block_size):
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subx = x[i][j : j + block_size]
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x[i][j : j + block_size] = fwt(subx)
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return x.reshape(_x.shape)
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# print(f"ffn1_w {ffn1_w.shape}: {ffn1_w}")
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# ffn1_quant_w8, ffn1_w8_scale = weight_quant(ffn1_w, "weight_only_int8")
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# ffn1_quant_w4, ffn1_w4_scale = weight_quant(ffn1_w, "weight_only_int4")
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# print(f"ffn1_quant_w8 {ffn1_quant_w8.shape}: {ffn1_quant_w8}")
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# print(f"ffn1_w8_scale {ffn1_w8_scale.shape}: {ffn1_w8_scale}")
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# print(f"ffn1_quant_w4 {ffn1_quant_w4.shape}: {ffn1_quant_w4}")
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# print(f"ffn1_w4_scale {ffn1_w4_scale.shape}: {ffn1_w4_scale}")
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# ffn1_w8_dq = weight_dequant(ffn1_quant_w8, ffn1_w8_scale, "weight_only_int8")
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# ffn1_w4_dq = weight_dequant(ffn1_quant_w4, ffn1_w4_scale, "weight_only_int4")
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# print(f"ffn1_w8_dq {ffn1_w8_dq.shape}: {ffn1_w8_dq}")
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# print(f"ffn1_w4_dq {ffn1_w4_dq.shape}: {ffn1_w4_dq}")
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def batch_matmul(x, token_num_info, w, w_scale, algo):
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w_fp32 = weight_dequant(w, w_scale, algo)
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print(f"x {x.shape}, w {w_fp32.shape}")
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out_hidden_dim = w_fp32.shape[1]
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if not used_in_ep_low_latency:
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y = np.zeros([x.shape[0], out_hidden_dim], "float32")
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token_num_lod = token_num_info
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for i in range(expert_num_per_rank):
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start_i = token_num_lod[i]
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end_i = token_num_lod[i + 1]
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subx = x[start_i:end_i]
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subw = w_fp32[i : i + 1].transpose([0, 2, 1])
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y[start_i:end_i] = np.matmul(subx, subw)
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else:
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y = np.zeros(
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[
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expert_num_per_rank,
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num_max_dispatch_tokens_per_rank,
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out_hidden_dim,
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],
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"float32",
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)
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token_num_per_expert = token_num_info
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for i in range(expert_num_per_rank):
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subx = x[i][: token_num_per_expert[i]]
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subw = w_fp32[i : i + 1].transpose([0, 2, 1])
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y[i][: token_num_per_expert[i]] = np.matmul(subx, subw)
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return y
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def swiglu(x):
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new_shape = list(x.shape)
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new_shape[-1] //= 2
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x1 = np.copy(x[..., : new_shape[-1]])
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x2 = np.copy(x[..., new_shape[-1] :])
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y = x1 * 1.0 / (1.0 + np.exp(-x1)) * x2
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return y
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ref_ffn1_out = batch_matmul(ffn_in, token_num_info, ffn1_quant_w, ffn1_w_scale, quant_method)
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print(f"ref_ffn1_out {ref_ffn1_out.shape}: {ref_ffn1_out}")
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ref_swiglu_out = swiglu(ref_ffn1_out)
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print(f"ref_swiglu_out {ref_swiglu_out.shape}: {ref_swiglu_out}")
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ref_swiglu_out = (ref_swiglu_out + ffn2_shift) * ffn2_smooth
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ref_hadamard_out = hadamard(ref_swiglu_out, hadamard_blocksize)
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ref_ffn2_out = batch_matmul(
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ref_hadamard_out,
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token_num_info,
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ffn2_quant_w,
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ffn2_w_scale,
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quant_method,
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)
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ffn_in_tensor = paddle.to_tensor(ffn_in).astype("bfloat16")
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token_num_info_tensor = paddle.to_tensor(token_num_info)
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ffn1_quant_w_tensor = paddle.to_tensor(ffn1_quant_w)
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ffn2_quant_w_tensor = paddle.to_tensor(ffn2_quant_w)
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ffn1_w_scale_tensor = paddle.to_tensor(ffn1_w_scale)
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ffn2_w_scale_tensor = paddle.to_tensor(ffn2_w_scale)
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ffn2_shift_tensor = paddle.to_tensor(ffn2_shift).astype("bfloat16")
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ffn2_smooth_tensor = paddle.to_tensor(ffn2_smooth).astype("bfloat16")
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ffn2_out = moe_expert_ffn(
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ffn_in_tensor,
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token_num_info_tensor,
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ffn1_quant_w_tensor,
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ffn2_quant_w_tensor,
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None, # ffn1_bias
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None, # ffn2_bias
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None, # ffn1_act_scale
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None, # ffn2_act_scale
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ffn1_w_scale_tensor,
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ffn2_w_scale_tensor,
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ffn2_shift_tensor,
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ffn2_smooth_tensor,
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quant_method,
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hadamard_blocksize,
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token_num,
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)
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ffn2_out = ffn2_out.astype("float32").numpy()
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print(f"ffn2_out: {ffn2_out}")
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print(f"ref_ffn2_out: {ref_ffn2_out}")
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if not used_in_ep_low_latency:
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diff = np.sum(np.abs(ffn2_out - ref_ffn2_out)) / np.sum(np.abs(ffn2_out))
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print(f"diff: {diff}")
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assert diff < 0.01, f"diff: {diff}\nffn2_out:\n{ffn2_out}\nref_ffn2_out:\n{ref_ffn2_out}\n"
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else:
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diff_all = 0
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for i in range(expert_num_per_rank):
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token_num_this_expert = token_num_per_expert[i]
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if token_num_this_expert == 0:
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continue
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tmp_ffn2_out = ffn2_out[i][:token_num_this_expert]
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tmp_ref_ffn2_out = ref_ffn2_out[i][:token_num_this_expert]
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diff = np.sum(np.abs(tmp_ffn2_out - tmp_ref_ffn2_out)) / np.sum(np.abs(tmp_ffn2_out))
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print(f"diff: {diff}")
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print(f"{i}, tmp_ffn2_out: {tmp_ffn2_out}")
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print(f"{i}, tmp_ref_ffn2_out: {tmp_ref_ffn2_out}")
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diff_all += diff
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diff_avg = diff_all / expert_num_per_rank
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print(f"diff_avg: {diff_avg}")
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assert diff_avg < 0.03, f"diff_avg: {diff_avg}\nffn2_out:\n{ffn2_out}\nref_ffn2_out:\n{ref_ffn2_out}\n"
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