mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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142 lines
4.9 KiB
Python
142 lines
4.9 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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 unittest
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# isort: off
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import torch
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# isort: on
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import os
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import sys
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from parameterized import parameterized
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import tensorrt_llm
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from tensorrt_llm import Tensor
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import (create_session, run_session, skip_bf16_pre_ampere,
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unittest_name_func)
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class TestMatmul(unittest.TestCase):
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def setUp(self):
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torch.backends.cudnn.allow_tf32 = False
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tensorrt_llm.logger.set_level('error')
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def _matmul(self, m, n, k, dtype, ta, tb):
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shape1 = (k, m) if ta else (m, k)
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mat1 = torch.randn(shape1,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
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device="cuda") * 1e-1
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shape2 = (n, k) if tb else (k, n)
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mat2 = torch.randn(shape2,
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dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
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device="cuda") * 1e-1
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# construct trt network
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builder = tensorrt_llm.Builder()
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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x = Tensor(name='x',
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shape=mat1.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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y = Tensor(name='y',
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shape=mat2.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.matmul(x, y, transa=ta, transb=tb)
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output.mark_output('output', dtype)
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# trt run
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session = create_session(builder, network, precision=dtype)
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inputs = {'x': mat1, 'y': mat2}
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outputs = run_session(session, inputs)
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tols = {
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"float32": {
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"rtol": 4e-4,
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"atol": 1e-02
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},
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"float16": {
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"rtol": 1e-02,
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"atol": 1e-02
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},
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"bfloat16": {
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"rtol": 1e-02,
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"atol": 1e-02
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},
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}
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# pytorch run
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if ta:
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mat1 = mat1.transpose(0, 1)
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if tb:
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mat2 = mat2.transpose(0, 1)
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ref = torch.matmul(mat1, mat2)
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torch.testing.assert_close(ref, outputs['output'], **tols[dtype])
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@parameterized.expand([('float16', False, False), ('float16', False, True),
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('float16', True, False), ('float16', True, True),
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('bfloat16', True, False), ('bfloat16', True, True),
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('float32', False, False), ('float32', False, True),
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('float32', True, False), ('float32', True, True)],
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name_func=unittest_name_func)
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def test_matmul(self, dtype, transa, transb):
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# Skip tests that are not supported in pre-ampere architecture
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skip_bf16_pre_ampere(dtype)
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bs = 2
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inseq = 16
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hidden_size = 768
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tp = 1
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# qkv_gemm
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self._matmul(bs * inseq, 3 * hidden_size // tp, hidden_size, dtype,
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transa, transb)
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# mlp_gemm_1
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self._matmul(bs * inseq, 4 * hidden_size // tp, hidden_size, dtype,
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transa, transb)
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def test_matmul_broadcast(self):
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dtype = 'float32'
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x_data = torch.randn(16, 4, 4, 5, device="cuda")
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y_data = torch.randn(16, 1, 5, 4, device="cuda")
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# construct trt network
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builder = tensorrt_llm.Builder()
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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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x = Tensor(name='x',
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shape=x_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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y = Tensor(name='y',
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shape=y_data.shape,
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dtype=tensorrt_llm.str_dtype_to_trt(dtype))
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output = tensorrt_llm.functional.matmul(x, y)
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output.mark_output('output', dtype)
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# trt run
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session = create_session(builder, network, precision=dtype)
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inputs = {'x': x_data, 'y': y_data}
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outputs = run_session(session, inputs)
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# pytorch run
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ref = torch.matmul(x_data, y_data)
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# compare diff
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torch.testing.assert_close(ref, outputs['output'])
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