Combining Manual Pipeline Parallelism & Automatic SPMD Parallelism #46
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Closed
merrymercy
reviewed
Jul 8, 2021
| @@ -94,7 +94,7 @@ def loss_func(params): | |||
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| hidden_states = jnp.ones((batch_size, seq_len, hidden_size), dtype=jnp.float32) | |||
| attention_mask = jnp.ones((batch_size, seq_len), dtype=jnp.int32) | |||
| label = jnp.ones((batch_size, seq_len, hidden_size), dtype=jnp.float32) | |||
| label = jnp.ones((batch_size, seq_len, hidden_size), dtype=jnp.float32) * 23.0 * np.arange(hidden_size)[None, None, :] | |||
| @@ -0,0 +1,11 @@ | |||
| # XLA Pipeline Marker Custom Call | |||
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Adding a custom call is not as simple as I thought. How can we simplify this? For example, can putting the code to tensorflow-parax simplify the compilation process?
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| xla_computation = xla_client.XlaComputation(hlo_proto) | ||
| num_devices = np.prod(strategy_config.logical_mesh_shape) | ||
| assert num_devices == len(self.backend.devices()) | ||
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| compiled = compile_with_given_strategy( | ||
| compiled = compile_without_auto_sharding( |
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This is not always correct. When not using 3d parallel, we will pass an unoptimized HLO Proto. In this case, we need to call compile_with_given_strategy. I can fix this for you later.
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