[DRAFT] Mh/jk/diffusion full pipeline forecast#2396
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MatKbauer
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Two small modifications, but looks good. Currently training some models to see convergence.
| tokens, posteriors = self.encoder.encoder(model_params=model_params, batch=batch) | ||
| shape = (len(batch), batch.get_num_steps(), *tokens.shape[1:]) | ||
| tokens_multi = tokens.reshape(shape) | ||
| tokens = tokens_multi[:, -1] |
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Let's revert this back again
| # Reshape tokens to [B, T, ...] | ||
| tokens = tokens.reshape(shape) | ||
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| if self.cf.get("fe_diffusion_model", False): |
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To allow unconditional and non-forecast conditional training, this check should be
if self.cf.get("fe_diffusion_model_conditioning", None) == "forecast":
MatKbauer
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Some more suggestions to make code more robust
| self.streams = cf.streams | ||
| self.rank = cf.rank | ||
| self.world_size = cf.world_size | ||
| self.diffusion_model_conditioning = cf.fe_diffusion_model_conditioning |
| embedding_dim=self.embedding_dim, frequency_embedding_dim=self.frequency_embedding_dim | ||
| ) | ||
| self.datetime_embedder = DateTimeEncoder() | ||
| self.conditioning = self.cf.fe_diffusion_model_conditioning |
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self.cf.get("fe_diffusion_model_conditioning", None")
| if self.cf.fe_diffusion_model_conditioning in ["date_time", "date", "time"]: | ||
| c = meta_info["ERA5"].params["timestamp"] | ||
| elif self.cf.fe_diffusion_model_conditioning == "forecast": | ||
| c = meta_info["ERA5"].params["conditioning_tokens"] # X_{t-1} as conditioning (model.py extracts last step as target, passes second-to-last here) |
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self.conditioning in both places
| # Extract conditioning from meta_info (same as training_forward) | ||
| # Extract conditioning (mirrors training_forward). | ||
| c = None | ||
| if self.cf.fe_diffusion_model_conditioning in ["date_time", "date", "time"]: |
| qk_norm_type=self.cf.get("qk_norm_type", self.cf.norm_type), | ||
| norm_eps=self.cf.norm_eps, | ||
| attention_dtype=get_dtype(self.cf.attention_dtype), | ||
| is_dit=self.cf.fe_diffusion_model, |
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Let's also make this a self.cf.get("fe_diffusion_model", False)
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Description
DRAFT PR to assess diff between my conditioning branch and the current main diffusion branch.
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./scripts/actions.sh lint./scripts/actions.sh unit-test./scripts/actions.sh integration-testlaunch-slurm.py --time 60