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https://github.com/NVIDIA/TensorRT-LLM.git
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2d234357c6
* Update TensorRT-LLM --------- Co-authored-by: Altair-Alpha <62340011+Altair-Alpha@users.noreply.github.com>
178 lines
8.0 KiB
Python
178 lines
8.0 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 os
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import sys
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import unittest
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import torch
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import tensorrt_llm
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import tensorrt_llm.models.redrafter
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import tensorrt_llm.models.redrafter.redrafter_helper
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from tensorrt_llm import Tensor
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sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
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from utils.util import create_session, run_session, set_input_shapes
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class TestReDrafter(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('warning')
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def test_validate(self):
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bs = 2
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nb = 3
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bl = 4
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V = 4
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S = max(7, 9)
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old_device = torch.get_default_device()
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torch.set_default_device("cuda")
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torch.manual_seed(0)
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greedy_search = True
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draft_probs = torch.rand((bs, nb, bl - 1, V), dtype=torch.float32)
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draft_tokens = torch.tensor([[
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[91, 92, 93, 95],
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[91, 92, 94, 96],
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[91, 92, 93, 97],
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], [
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[93, 94, 95, 92],
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[93, 95, 96, 93],
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[93, 94, 97, 96],
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]],
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dtype=torch.int32) % V
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draft_tokens = torch.randint(10, size=(bs, nb, bl),
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dtype=torch.int32) % V
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draft_indices = torch.tensor([[
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[0, 1, 2, 3],
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[0, 1, 4, 5],
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[0, 1, 2, 6],
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], [
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[0, 1, 2, 3],
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[0, 4, 5, 6],
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[0, 1, 7, 8],
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]],
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dtype=torch.int32) % S
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draft_indices = torch.randint(10, size=(bs, nb, bl),
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dtype=torch.int32) % S
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flattened_logits = torch.rand((bs, S, V), dtype=torch.float32)
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rand_data = torch.rand((bs, nb, bl - 1), dtype=torch.float32)
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# ref outputs
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ref_max = torch.tensor([0, 1], dtype=torch.int32)
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ref_beam = torch.tensor([0, 0], dtype=torch.int32)
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ref_probs = torch.tensor([[
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[[-0., -50000., -50000., -50000.], [-0., -50000., -50000., -50000.],
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[-50000., -50000., -0., -50000.]],
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[[-50000., -0., -50000., -50000.], [-50000., -50000., -50000., -0.],
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[-50000., -50000., -50000., -0.]],
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[[-0., -50000., -50000., -50000.], [-50000., -50000., -0., -50000.],
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[-50000., -0., -50000., -50000.]]
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],
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[[[-50000., -0., -50000., -50000.],
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[-0., -50000., -50000., -50000.],
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[-0., -50000., -50000., -50000.]],
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[[-50000., -50000., -0., -50000.],
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[-50000., -50000., -0., -50000.],
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[-0., -50000., -50000., -50000.]],
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[[-50000., -0., -50000., -50000.],
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[-50000., -50000., -0., -50000.],
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[-50000., -50000., -0., -50000.]]]],
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dtype=torch.float32)
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ref_last_probs = torch.tensor([[[-50000., -50000., -50000., -0.],
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[-50000., -50000., -0., -50000.],
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[-50000., -50000., -50000., -0.]],
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[[-50000., -50000., -0., -50000.],
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[-0., -50000., -50000., -50000.],
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[-50000., -0., -50000., -50000.]]],
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dtype=torch.float32)
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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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draft_probs_t = Tensor(name='draft_probs',
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shape=[-1] + list(draft_probs.shape[1:]),
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dtype=tensorrt_llm.torch_dtype_to_trt(
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draft_probs.dtype))
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draft_tokens_t = Tensor(name='draft_tokens',
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shape=[-1] + list(draft_tokens.shape[1:]),
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dtype=tensorrt_llm.torch_dtype_to_trt(
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draft_tokens.dtype))
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draft_indices_t = Tensor(name='draft_indices',
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shape=[-1] + list(draft_indices.shape[1:]),
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dtype=tensorrt_llm.torch_dtype_to_trt(
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draft_indices.dtype))
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flattened_logits_t = Tensor(
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name='flattened_logits',
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shape=[-1, -1] + list(flattened_logits.shape[2:]),
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dtype=tensorrt_llm.torch_dtype_to_trt(flattened_logits.dtype))
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rand_data_t = Tensor(name='rand_data',
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shape=[-1] + list(rand_data.shape[1:]),
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dtype=tensorrt_llm.torch_dtype_to_trt(
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rand_data.dtype))
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outputs = tensorrt_llm.models.redrafter.redrafter_helper._validate_draft_tokens(
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draft_probs_t, draft_tokens_t, draft_indices_t,
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flattened_logits_t, nb, bl, greedy_search, rand_data_t)
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outputs[0].mark_output('max_num_accept_tokens')
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outputs[1].mark_output('beam_index')
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outputs[2].mark_output('base_log_probs')
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outputs[3].mark_output('last_base_log_probs')
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# trt run
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profile = builder.trt_builder.create_optimization_profile()
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set_input_shapes(profile, draft_probs_t, [0, nb, bl - 1, V],
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[16, nb, bl - 1, V], [32, nb, bl - 1, V])
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set_input_shapes(profile, draft_indices_t, [0, nb, bl], [16, nb, bl],
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[32, nb, bl])
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set_input_shapes(profile, draft_tokens_t, [0, nb, bl], [16, nb, bl],
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[32, nb, bl])
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set_input_shapes(profile, rand_data_t, [0, nb, bl - 1],
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[16, nb, bl - 1], [32, nb, bl - 1])
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set_input_shapes(profile, flattened_logits_t, [1, 1, V], [16, 8, V],
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[32, 16, V])
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session = create_session(builder,
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network,
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precision='float32',
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optimization_profiles=[profile])
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inputs = {
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'draft_probs': draft_probs,
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'draft_tokens': draft_tokens,
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'draft_indices': draft_indices,
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'rand_data': rand_data,
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'flattened_logits': flattened_logits,
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}
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outputs = run_session(session, inputs)
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# compare diff
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torch.testing.assert_close(ref_max,
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outputs['max_num_accept_tokens'],
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atol=0,
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rtol=0)
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torch.testing.assert_close(ref_beam,
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outputs['beam_index'],
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atol=0,
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rtol=0)
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torch.testing.assert_close(ref_probs,
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outputs['base_log_probs'],
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atol=0.01,
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rtol=0.1)
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torch.testing.assert_close(ref_last_probs,
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outputs['last_base_log_probs'],
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atol=0.01,
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rtol=0.1)
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torch.set_default_device(old_device)
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return
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