mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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308 lines
12 KiB
Python
308 lines
12 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 _utils
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import numpy as np
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import tensorrt as trt
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import torch
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from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner
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from polygraphy.logger import G_LOGGER
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from tensorrt_llm.quantization.quantize import (qserve_pack_reorder_per_channel,
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qserve_pack_reorder_per_group)
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G_LOGGER.severity = 0
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import tensorrt_llm
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from tensorrt_llm import Tensor
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from tensorrt_llm.quantization.functional import (qserve_gemm_per_channel,
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qserve_gemm_per_group)
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from utils.util import skip_pre_ampere
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class TestQServeGemm(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('error')
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def _case_qserve_gemm_per_group(self,
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m,
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n,
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k,
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group_size=128,
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seed=123456,
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dtype="float16"):
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torch.manual_seed(seed)
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# Initialize qact (int8 range: -128 to 127)
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qact = torch.randint(-128, 128, (m, k), dtype=torch.int8)
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# Initialize act_scales (float16)
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act_scales = torch.rand(
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m, 1, dtype=torch.float16) * 0.1 # small positive values
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# Initialize qweight (int4 range: 0 to 15, as per qserve_quantize_weight)
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qweight = torch.randint(0, 16, (n, k), dtype=torch.int8)
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# Initialize s1_scales (float16)
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s1_scales = torch.rand(n, 1, dtype=torch.float16) * 0.1
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# Initialize s2_scales (int)
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s2_scales = torch.randint(1, 16, (n, k // group_size), dtype=torch.int8)
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# Initialize s2_zeros (int)
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s2_zeros = torch.randint(0, 16, (n, k // group_size), dtype=torch.int8)
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# Ground truth matmul
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ref = _utils.gt_qserve_gemm_per_group(qact, act_scales, qweight,
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s1_scales, s2_scales,
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s2_zeros).cpu().numpy()
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# Prepare data for QServe gemm kernel
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qweight, s1_scales, s2_scales, s2_zeros = qserve_pack_reorder_per_group(
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qweight, s1_scales, s2_scales, s2_zeros, group_size)
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# Create builder
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builder = tensorrt_llm.Builder()
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builder.strongly_typed = False # Test need to run in weekly typed mode
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# Create empty network
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network = builder.create_network()
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# Allow SQ plugin of dtype type
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network.plugin_config.set_qserve_plugins("float16")
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with tensorrt_llm.net_guard(network):
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qact_trt = Tensor(
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name='qact',
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shape=qact.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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act_scales_trt = Tensor(
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name='act_scales',
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shape=act_scales.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("float16"))
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qweight_trt = Tensor(
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name='qweight',
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shape=qweight.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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s1_scales_trt = Tensor(
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name='s1_scales',
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shape=s1_scales.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("float16"))
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s2_scales_trt = Tensor(
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name='s2_scales',
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shape=s2_scales.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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s2_zeros_trt = Tensor(
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name='s2_zeros',
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shape=s2_zeros.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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output = qserve_gemm_per_group(qact_trt, act_scales_trt,
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qweight_trt, s1_scales_trt,
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s2_scales_trt, s2_zeros_trt)
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output.mark_output('output', dtype)
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engine = EngineFromNetwork(
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(builder.trt_builder, network.trt_network),
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config=CreateConfig(int8=True,
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fp16=True,
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memory_pool_limits={
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trt.MemoryPoolType.WORKSPACE:
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32 * 1024 * 1024
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}))
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# Infer engine
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with TrtRunner(engine) as runner:
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outputs = runner.infer(
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feed_dict={
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'qact': qact.numpy(),
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'act_scales': act_scales.numpy(),
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'qweight': qweight.numpy(),
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's1_scales': s1_scales.numpy(),
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's2_scales': s2_scales.numpy(),
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's2_zeros': s2_zeros.numpy()
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})
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output = outputs['output']
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# Allow difference by one code point.
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self.assertTrue(np.allclose(output, ref, rtol=0, atol=np.spacing(ref)))
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def _case_qserve_gemm_per_channel(self,
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m,
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n,
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k,
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seed=123456,
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dtype="float16"):
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torch.manual_seed(seed)
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# Initialize qact (int8 range: -128 to 127)
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qact = torch.randint(-128, 128, (m, k), dtype=torch.int8)
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# Initialize act_scales (float16)
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act_scales = torch.rand(
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m, 1, dtype=torch.float16) * 0.1 # small positive values
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# Compute act_sums
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act_sums = torch.sum(qact.float() * act_scales.float(),
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dim=1,
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keepdim=True).half()
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# Initialize qweight (int4 range: 0 to 15, as per qserve_quantize_weight)
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qweight = torch.randint(0, 16, (n, k), dtype=torch.int8)
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# Initialize s1_scales (float16)
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s1_scales = torch.rand(n, 1, dtype=torch.float16) * 0.1
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# Initialize s1_szeros (float16)
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s1_zeros = torch.randint(1, 16, (n, 1)).half()
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# Ground truth matmul
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ref = _utils.gt_qserve_gemm_per_channel(qact, act_scales, act_sums,
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qweight, s1_scales,
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s1_zeros).cpu().numpy()
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# Prepare data for QServe gemm kernel
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qweight, s1_scales, s1_szeros = qserve_pack_reorder_per_channel(
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qweight, s1_scales, s1_zeros)
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# Create builder
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builder = tensorrt_llm.Builder()
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builder.strongly_typed = False # Test need to run in weekly typed mode
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# Create empty network
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network = builder.create_network()
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# Allow SQ plugin of dtype type
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network.plugin_config.set_qserve_plugins("float16")
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with tensorrt_llm.net_guard(network):
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qact_trt = Tensor(
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name='qact',
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shape=qact.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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act_scales_trt = Tensor(
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name='act_scales',
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shape=act_scales.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("float16"))
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act_sums_trt = Tensor(
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name='act_sums',
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shape=act_scales.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("float16"))
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qweight_trt = Tensor(
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name='qweight',
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shape=qweight.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("int8"))
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s1_scales_trt = Tensor(
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name='s1_scales',
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shape=s1_scales.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("float16"))
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s1_szeros_trt = Tensor(
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name='s1_szeros',
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shape=s1_szeros.shape,
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dtype=tensorrt_llm._utils.str_dtype_to_trt("float16"))
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output = qserve_gemm_per_channel(qact_trt, act_scales_trt,
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act_sums_trt, qweight_trt,
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s1_scales_trt, s1_szeros_trt)
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output.mark_output('output', dtype)
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engine = EngineFromNetwork(
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(builder.trt_builder, network.trt_network),
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config=CreateConfig(int8=True,
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fp16=True,
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memory_pool_limits={
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trt.MemoryPoolType.WORKSPACE:
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32 * 1024 * 1024
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}))
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# Infer engine
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with TrtRunner(engine) as runner:
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outputs = runner.infer(
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feed_dict={
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'qact': qact.numpy(),
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'act_scales': act_scales.numpy(),
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'act_sums': act_sums.numpy(),
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'qweight': qweight.numpy(),
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's1_scales': s1_scales.numpy(),
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's1_szeros': s1_szeros.numpy(),
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})
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output = outputs['output']
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# Allow some difference.
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self.assertTrue(np.allclose(output, ref, rtol=1e-2, atol=0.25))
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@skip_pre_ampere
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def test_qserve_gemm_per_group(self, dtype='float16'):
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bs = 2
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inseq = 16
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hidden_size = 768
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# qkv_gemm
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self._case_qserve_gemm_per_group(bs * inseq,
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3 * hidden_size,
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hidden_size,
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dtype=dtype)
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# mlp_gemm_1
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self._case_qserve_gemm_per_group(bs * inseq,
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4 * hidden_size,
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hidden_size,
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dtype=dtype)
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@skip_pre_ampere
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def test_qserve_gemm_per_channel(self, dtype='float16'):
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bs = 2
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inseq = 16
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hidden_size = 768
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# qkv_gemm
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self._case_qserve_gemm_per_channel(bs * inseq,
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3 * hidden_size,
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hidden_size,
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dtype=dtype)
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# mlp_gemm_1
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self._case_qserve_gemm_per_channel(bs * inseq,
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4 * hidden_size,
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hidden_size,
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dtype=dtype)
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def test_qserve_gemm_per_group_no_plugin(self):
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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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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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# Gemm ootb should fail
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with self.assertRaisesRegex(
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TypeError,
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"QServe Quant GEMM is only supported with plugin"):
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qserve_gemm_per_group(None, None, None, None, None, None)
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def test_qserve_gemm_per_channel_no_plugin(self):
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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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network = builder.create_network()
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with tensorrt_llm.net_guard(network):
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# Gemm ootb should fail
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with self.assertRaisesRegex(
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TypeError,
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"QServe Quant GEMM is only supported with plugin"):
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qserve_gemm_per_channel(None, None, None, None, None, None)
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if __name__ == '__main__':
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unittest.main()
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