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This PR introduces *DeepCompile*, a new feature that efficiently integrates compiler optimizations with other DeepSpeed features. DeepCompile utilizes torch's dynamo to capture the computation graph and modifies it to incorporate DeepSpeed’s optimizations seamlessly. Currently, DeepCompile supports ZeRO-1 and ZeRO-3, with enhancements such as proactive prefetching and selective unsharding to improve performance. (More details will be added later.) --------- Signed-off-by: Masahiro Tanaka <mtanaka@microsoft.com> Signed-off-by: Olatunji Ruwase <olruwase@microsoft.com> Co-authored-by: zafarsadiq <zafarsadiq120@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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from ..profilers.graph_profile import MemoryProfilingInterpreter
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import deepspeed.comm as dist
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def run_opt_passes(nz3,
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graph_index,
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graph_id,
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gm,
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create_inputs_fn,
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opt_passes,
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graph_order,
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profiling_results,
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param_manager,
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bwd,
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debug_log=False):
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profile = profiling_results[graph_id]
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rank = dist.get_rank()
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for i, opt_pass in enumerate(opt_passes):
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opt_pass_fn, mem_budget = opt_pass
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graph = opt_pass_fn(gm.graph, graph_id, graph_order, profiling_results, mem_budget, param_manager, bwd)
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graph.lint()
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gm.graph = graph
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gm.recompile()
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if debug_log:
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print(f"Prefetching enabled for {'bwd' if bwd else 'fwd'} graph_id={graph_id} {graph}")
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mem_prof = MemoryProfilingInterpreter(nz3, gm)
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mem_prof.run(*create_inputs_fn())
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if debug_log and rank == 0:
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mem_prof.dump(f"mem_prof_r{rank}_{'bwd' if bwd else 'fwd'}_{graph_index}_{graph_id}_pass_{i}.csv")
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mem = [(name, current_alloc, delta, peak) for name, current_alloc, delta, peak in mem_prof.mem_record]
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if bwd:
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profile.bwd_mem = mem
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else:
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profile.fwd_mem = mem
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return gm
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