b2e89e6e80
[FT] feat: Add FasterTransformer v3.0 1. Add supporting of INT8 quantization of cpp and TensorFlow op. 2. Provide the tools to quantize the model. 3. Fix the bugs that cmake 3.15 and 3.16 cannot build this project. 4. Deprecate the FasterTransformer v1
156 lines
5 KiB
Python
156 lines
5 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. 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 tensorflow as tf
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import numpy as np
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import os,sys
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sys.path.append(os.getcwd())
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from utils.sampling import Sampling
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sample_number = 100000
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np.random.seed(1)
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tf.set_random_seed(1)
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def top_k_sampling_unit_test():
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top_k_sampling = Sampling("top_k")
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probs_1 = np.asarray([ [4.0, 3.0, 2.0, 1.0] ])
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np_result_1 = [0]
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k_1 = 1
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tf_result_1 = top_k_sampling.sample(tf.convert_to_tensor(probs_1), k_1)
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probs_2 = np.asarray(np.log([ [0.6, 0.4, 0.3, 0.1] ]))
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np_result_2 = [0.6, 0.4, 0.0, 0.0]
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k_2 = 2
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tf_result_2 = top_k_sampling.sample(tf.convert_to_tensor(probs_2), k_2, sample_number)
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np_probs_3 = [0.3, 0.4, 0.25, 0.01, 0.05]
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probs_3 = np.asarray(np.log([np_probs_3]))
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np_result_3 = [0.3, 0.4, 0.25, 0.0, 0.05]
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k_3 = 4
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tf_result_3 = top_k_sampling.sample(tf.convert_to_tensor(probs_3), k_3, sample_number)
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np_probs_4 = [0.3, 0.4, 0.25, 0.01, 0.05]
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probs_4 = np.asarray(np.log([np_probs_4]))
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np_result_4 = [0.3/0.7, 0.4/0.7, 0.0, 0.00, 0.00]
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k_4 = 2
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tf_result_4 = top_k_sampling.sample(tf.convert_to_tensor(probs_4), k_4, sample_number)
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np_probs_5 = np.random.randn(1, 10000) * 1
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np_probs_5 = np.abs(np_probs_5)
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np_probs_5[0][0] *= 5
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np_result_5 = np_probs_5
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k_5 = 10
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np_sorted_result_5 = np.sort(np_probs_5)
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threshold = np_sorted_result_5[:,-k_5]
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mask = np_probs_5 >= threshold
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np_result_5 = np_probs_5 * mask
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np_result_5[0] = np_result_5[0] / np.sum(np_result_5[0])
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tf_result_5 = top_k_sampling.sample(tf.convert_to_tensor(np.log(np_probs_5)), k_5, sample_number)
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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print("[INFO] start the top k sampling unit test.")
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with tf.Session(config=config) as sess:
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tf_result_1 = sess.run(tf_result_1)
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tf_result_1 = np.asarray(tf_result_1).astype(np.int32)
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print("[INFO] case_1.")
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for i, j in zip(tf_result_1, np_result_1):
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assert(i == j)
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print("[INFO] case_2.")
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tf_result_2 = sess.run(tf_result_2)
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p0 = 0
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p1 = 0
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for i in tf_result_2:
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if i == 0:
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p0 += 1
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elif i == 1:
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p1 += 1
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print(p0/sample_number, p1/sample_number)
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print(np_result_2)
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print("[INFO] case_3.")
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tf_result_3 = sess.run(tf_result_3)
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p = np.zeros_like(np_result_3)
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for i in tf_result_3:
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p[i] += 1
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print(p * 1.0 / sample_number)
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print(np_result_3)
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print("[INFO] case_4.")
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tf_result_4 = sess.run(tf_result_4)
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p = np.zeros_like(np_result_4)
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for i in tf_result_4:
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p[i] += 1
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print(p * 1.0 / sample_number)
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print(np_result_4)
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print("[INFO] case_5.")
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tf_result_5 = sess.run(tf_result_5)
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p = np.zeros_like(np_result_5)
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print(tf_result_5)
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for i in tf_result_5:
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p[0][i] += 1
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for i, j in zip(p[0]/sample_number, np_result_5[0]):
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if i != 0 or j != 0:
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print(i, j)
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def top_p_sampling_unit_test():
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top_p_sampling = Sampling("top_p")
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np_probs_1 = [0.3, 0.01, 0.4, 0.25, 0.05]
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np_result_1 = [0, 0, 1, 0, 0]
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p_1 = 0.3
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tf_result_1 = top_p_sampling.sample(tf.convert_to_tensor(np.log([np_probs_1])), p_1, sample_number)
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np_probs_2 = [0.3, 0.01, 0.4, 0.25, 0.05]
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np_result_2 = [3./7, 0, 4./7, 0, 0]
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p_2 = 0.5
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tf_result_2 = top_p_sampling.sample(tf.convert_to_tensor(np.log([np_probs_2])), p_2, sample_number)
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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print("[INFO] start the top p sampling unit test.")
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with tf.Session(config=config) as sess:
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print("[INFO] case_1.")
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tf_result_1 = sess.run(tf_result_1)
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p = np.zeros_like(np_result_1)
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for i in tf_result_1:
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for j in range(len(p)):
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if i == j:
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p[j] += 1
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print(p * 1.0 / sample_number)
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print(np_result_1)
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print("[INFO] case_2.")
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tf_result_2 = sess.run(tf_result_2)
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p = np.zeros_like(np_result_2)
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for i in tf_result_2:
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for j in range(len(p)):
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if i == j:
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p[j] += 1
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print(p * 1.0 / sample_number)
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print(np_result_2)
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if __name__ == "__main__":
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top_k_sampling_unit_test()
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top_p_sampling_unit_test()
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