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https://github.com/NVIDIA/TensorRT-LLM.git
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258c7540c0
Co-authored-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> open source f8c0381a2bc50ee2739c3d8c2be481b31e5f00bd (#2736) Co-authored-by: Kaiyu Xie <26294424+kaiyux@users.noreply.github.com> Add note for blackwell (#2742) Update the docs to workaround the extra-index-url issue (#2744) update README.md (#2751) Fix github io pages (#2761) Update
117 lines
4.2 KiB
Python
117 lines
4.2 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 numpy as np
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import pytest
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import torch
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import tensorrt_llm
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from tensorrt_llm import Tensor
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from tensorrt_llm._utils import str_dtype_to_torch, str_dtype_to_trt
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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# Monkey Patching for torch.float8_e4m3fn support
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from polygraphy.datatype import DataType
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from utils.util import getSMVersion
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original_to_dtype = DataType.to_dtype
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def patched_to_dtype(dtype, target_module):
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if dtype == DataType.FLOAT8E4M3FN and target_module == 'torch':
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return torch.float8_e4m3fn
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else:
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return original_to_dtype(dtype, target_module)
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DataType.to_dtype = patched_to_dtype
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class TestLowLatencyGemm(unittest.TestCase):
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def setUp(self) -> None:
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tensorrt_llm.logger.set_level('error')
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# float32
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def run_low_latency_gemm_sm90(self, m, n, k, output_dtype):
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torch.random.manual_seed(42)
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shape_x = (m, k)
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shape_w = (n, k)
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x = torch.rand(shape_x, device="cuda").to(str_dtype_to_torch('fp8'))
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w = torch.rand(shape_w, device="cuda").to(str_dtype_to_torch('fp8'))
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# Create builder
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builder = tensorrt_llm.Builder()
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# Create empty network
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net = builder.create_network()
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net.plugin_config.low_latency_gemm_plugin = "fp8"
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with tensorrt_llm.net_guard(net):
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# Init TensorRT-LLM tensor for x
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x_tensor = Tensor(name='x',
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shape=x.shape,
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dtype=str_dtype_to_trt('fp8'))
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# Init TensorRT-LLM tensor for w
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w_tensor = Tensor(name='w',
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shape=w.shape,
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dtype=str_dtype_to_trt('fp8'))
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# Get output tensor
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output = tensorrt_llm.functional.low_latency_gemm(
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x_tensor, w_tensor, strict_dtype=str_dtype_to_trt(output_dtype))
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net._mark_output(output,
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'output',
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dtype=str_dtype_to_trt(output_dtype))
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feed_dict = {'x': x, "w": w}
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output_trt = torch.empty((m, n),
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device="cuda",
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dtype=str_dtype_to_torch(output_dtype))
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outputs = {'output': output_trt}
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stream = torch.cuda.current_stream()
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builder_config = builder.create_builder_config(precision=output_dtype)
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engine = builder.build_engine(net, builder_config)
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session = tensorrt_llm.runtime.Session.from_serialized_engine(engine)
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session.run(inputs=feed_dict,
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outputs=outputs,
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stream=stream.cuda_stream)
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torch.cuda.synchronize()
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ref = torch._scaled_mm(
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x,
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w.t(),
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scale_a=torch.tensor(1.0).cuda(),
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scale_b=torch.tensor(1.0).cuda(),
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out_dtype=str_dtype_to_torch(output_dtype),
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use_fast_accum=True,
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)
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np.testing.assert_allclose(ref.float().cpu(),
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outputs['output'].float().cpu())
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@pytest.mark.skipif(getSMVersion() != 90,
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reason="LowLatencyGemm is only supported in SM90"
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) # Skip tests that are not supported in SM90
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def test_low_latency_gemm(self):
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m = 64
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k = 8192
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n = 8192
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output_dtype = "float32"
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self.run_low_latency_gemm_sm90(m, n, k, output_dtype)
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output_dtype = "float16"
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self.run_low_latency_gemm_sm90(m, n, k, output_dtype)
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if __name__ == '__main__':
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unittest.main()
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