Enable 2D sharding #17
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jonb377
approved these changes
Aug 1, 2023
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| data_model_mesh = xs.Mesh(device_ids, (data, mod)) | ||
| model_data_mesh = xs.Mesh(device_ids, (mod, data)) |
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Can you try with HybridMesh? It should provide some performance gain, but I haven't actually benchmarked the difference. Here and in modeling_llama.py
@khatwanimohit may have some benchmarked differences on the simple shardings.py script
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Let me do that. Always forgot.
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Fixed that too.
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| elif 'gate_proj' in name or 'up_proj' in name: | ||
| xs.mark_sharding(param, data_model_mesh, range(len(param.shape))) | ||
| elif 'down_proj' in name: | ||
| xs.mark_sharding(param, model_data_mesh, range(len(param.shape))) |
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Just for my understanding: I noticed that HF shards gate_proj and up_proj on the 0th dim and down_proj on the 1st dim, but here you're sharding gate and up on the data_model mesh, which places the model axis on dim 1.
Is this just a difference in 1- and 2-D sharding?
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That's a good catch. I don't know. Let me dig into it. I'm following the slides attached on the top of the spreadsheet.
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No worries! I was just curious, using the sharding from the slides makes sense.
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Yea, you are right. I have corrected the error.
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Thanks Jon for approving the pull request. |
alanwaketan
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Aug 2, 2023
Summary: This pull requests fix a bug in #17 where it forgot to guard 2D sharding for activations and inputs. Test Plan: N/A.
alanwaketan
added a commit
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Oct 27, 2023
Summary: This pull request adds 2D SPMD sharding to the table. It will shard both weights and activations. Here is the sharding strategy. Let's say we have a 2D mesh (data, model) and data x model == num_devices: 1. input (data,, None, model) 2. embedding (model, data) 3. attn QKV (data, model) 4. attn O (model, data) 5. mlp gate, up (model, data) 6. mlp down (data, model) 7. activation (data,, None, model) Currently you can specify the model dimension using a new option --spmd_2d_sharding, then the data dimension will be auto-calculated. TODO: maybe we should have another option to specify whether or not we should shard the activations/inputs or shard them differently.
alanwaketan
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Oct 27, 2023
Summary: This pull requests fix a bug in #17 where it forgot to guard 2D sharding for activations and inputs. Test Plan: N/A.
yeounoh
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Mar 19, 2024
Summary: This pull request adds 2D SPMD sharding to the table. It will shard both weights and activations. Here is the sharding strategy. Let's say we have a 2D mesh (data, model) and data x model == num_devices: 1. input (data,, None, model) 2. embedding (model, data) 3. attn QKV (data, model) 4. attn O (model, data) 5. mlp gate, up (model, data) 6. mlp down (data, model) 7. activation (data,, None, model) Currently you can specify the model dimension using a new option --spmd_2d_sharding, then the data dimension will be auto-calculated. TODO: maybe we should have another option to specify whether or not we should shard the activations/inputs or shard them differently.
yeounoh
pushed a commit
that referenced
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Mar 19, 2024
Summary: This pull requests fix a bug in #17 where it forgot to guard 2D sharding for activations and inputs. Test Plan: N/A.
vanbasten23
pushed a commit
that referenced
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May 21, 2024
Summary: This pull request adds 2D SPMD sharding to the table. It will shard both weights and activations. Here is the sharding strategy. Let's say we have a 2D mesh (data, model) and data x model == num_devices: 1. input (data,, None, model) 2. embedding (model, data) 3. attn QKV (data, model) 4. attn O (model, data) 5. mlp gate, up (model, data) 6. mlp down (data, model) 7. activation (data,, None, model) Currently you can specify the model dimension using a new option --spmd_2d_sharding, then the data dimension will be auto-calculated. TODO: maybe we should have another option to specify whether or not we should shard the activations/inputs or shard them differently.
vanbasten23
pushed a commit
that referenced
this pull request
May 21, 2024
Summary: This pull requests fix a bug in #17 where it forgot to guard 2D sharding for activations and inputs. Test Plan: N/A.
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Summary:
This pull request adds 2D SPMD sharding to the table. It will shard both weights and activations. Here is the sharding strategy.
Let's say we have a 2D mesh (data, model) and data x model == num_devices:
Currently you can specify the model dimension using a new option --spmd_2d_sharding, then the data dimension will be auto-calculated.
TODO: maybe we should have another option to specify whether or not we should shard the activations/inputs or shard them differently.