U Jºc‰ã@sÄddlZddlZddlmZGdd„deƒZGdd„deƒZeejdœdd„Z eejdœd d „Z eejejd œd d „Z ej eejejdœdd„Z deejejd œdd„Zdeejejd œdd„ZdS)éNc@s<eZdZdZddddgZejdœdd„Zee d œd d „Z d S) Ú DefaultStatez¼ Stores state needed to perform the default communication algorithm within a communication hook. Args: process_group (ProcessGroup): The process group to be used. Ú process_groupÚ world_sizeÚgradient_predivide_factorÚgradient_postdivide_factor)rcCsJ|dkrtd|›dƒ‚||_t |¡|_| |j¡|_|j|j|_dS)Nz0Expected to pass in an explicit ProcessGroup to Ú.)Ú ValueErrorrÚdistZget_world_sizerÚ_get_gradient_predivide_factorrr)Úselfr©r úZ/tmp/pip-unpacked-wheel-gikjz4vx/torch/distributed/algorithms/_comm_hooks/default_hooks.pyÚ__init__s ÿzDefaultState.__init__)rÚreturncCs.d}||dkr&|||kr&|d9}qt|ƒS)Néré)Úfloat)r rZfactorr r r r %s z+DefaultState._get_gradient_predivide_factorN) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú __slots__r Z ProcessGrouprÚintrr r r r r rs ü þ rcs,eZdZdZdgZejf‡fdd„ Z‡ZS)ÚLowPrecisionStateaØ Stores state needed to perform gradient communication in a lower precision within a communication hook. Communication hook will cast gradients back to the original parameter precision specified by ``parameter_type`` (default: torch.float32). Builds on top of the :class:`DefaultState`. Args: parameter_type (torch.dtype): The precision of model's parameters. Required for a hook to cast gradients back to a parameter's precision. Úparameter_typecstƒ |¡||_dS©N)Úsuperrr)r rr©Ú __class__r r r;s zLowPrecisionState.__init__) rrrrrÚtorchZfloat32rÚ __classcell__r r rr r+s  ÿýr©ÚstateÚgradcCs*|j}|j |j¡|_| tj ¡¡dS)zu Casts gradients back to full parameter precision so that further computation happens in full precision. N)ÚdataÚtorZ record_streamrZcudaZcurrent_stream)r"r#Zorig_grad_datar r r Ú _decompressDsr&cCs@|jdkr| |j¡tj||jd|jdkr<| |j¡dS)aR This FSDP communication hook implements ``all_reduce`` algorithm and a necessary pre- and post-division of gradients. Args: state (DefaultState): State information, configures pre- and post-division factors. grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks. r©ÚgroupN)rÚdiv_r Z all_reducerrr!r r r Úallreduce_hookNs   r*©r"r#ÚoutputcCsB|jdkr| |j¡tj|||jd|jdkr>| |j¡dS)aä This FSDP communication hook implements ``reduce_scatter`` algorithm for sharded FSDP strategies and a necessary pre- and post-division of gradients. Args: state (DefaultState): State information, configures pre- and post-division factors. grad (torch.Tensor): An unsharded gradient for the local batch that needs to be communicated across ranks. output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``. rr'N)rr)r Z_reduce_scatter_baserrr+r r r Úreduce_scatter_hookas  ÿ r-©Úprecr"r#r,cCsT|j |¡|_|dk r<|j |¡|_t|||ƒt||ƒnt||ƒt||ƒdSr)r$r%r-r&r*r.r r r Ú_low_precision_hookvs   r0cCst ttj¡}||||ƒS©aƒ This FSDP communication hook implements a simple gradient compression approach that casts ``grad`` to half-precision floating-point format (``torch.float16``). It also averages gradients by ``world_size`` in two steps: first it pre-divides gradients by a ``state.gradient_predivide_factor``, and after a communication step (``all_reduce`` or ``reduce_scatter``) gradients are averaged by a ``state.gradient_postdivide_factor``. Once post-division is done, compressed gradients are casted back to parameters' precision. Args: state (LowPrecisionState): State information, configures pre- and post-division factors, parameters' precision. grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks in a lower precision. output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``. )Ú functoolsÚpartialr0rZfloat16)r"r#r,Z fp16_hookr r r Úfp16_compress_hook€sr4cCst ttj¡}||||ƒSr1)r2r3r0rZbfloat16)r"r#r,Z bf16_hookr r r Úbf16_compress_hook‘sr5)N)N)r2rZtorch.distributedZ distributedr ÚobjectrrZTensorr&r*r-Zdtyper0r4r5r r r r Ús %