Moment infini #76
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Moment infini #76
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added 2 commits
March 26, 2025 18:00
mononitogoswami
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mononitogoswami,
JanekDev and
zukowskanina
JanekDev
approved these changes
Mar 27, 2025
momentfm/models/moment.py
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| x_enc = self.tokenizer(x=x_enc) | ||
| batch_size, n_channels, seq_len = x_enc.shape |
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I suggest unifying n_channels and n_series.
momentfm/models/moment.py
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| x: [batch_size x n_channels x n_patches x d_model] | ||
| output: [batch_size x n_channels x forecast_horizon] | ||
| """ | ||
| x = self.flatten(x) # x: [batch_size, n_series, n_patches, d_model] |
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suggesting unification of n_channels and n_series
momentfm/utils/t5_infini.py
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| if not self.has_relative_attention_bias: | ||
| position_bias = torch.zeros( | ||
| (1, self.n_channels, self.n_heads, seq_length, key_length), device=hidden_states.device, dtype=hidden_states.dtype | ||
| ) # Willa - should we use n_channels or just 1? |
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in the original implementation by Nina there is no channel axis, so it gets probably broadcasted and position biases are shared between channels, hence there should be probably 1?
momentfm/utils/t5_infini.py
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| # Vectorized infini attention computation across channels | ||
| sigma_k = self.elu(key_states) + 1.0 # [batch_size, n_series, n_heads, n_patch, dim] | ||
| sigma_k_transposed = sigma_k.transpose(-2, -1) # [batch_size, n_series, n_heads, dim, n_patch] | ||
| memory_matrix = torch.matmul(sigma_k_transposed, value_states).sum(dim=1).unsqueeze(1) # [batch_size, 1, n_heads, dim, dim] sum over channels then unsqueeze to enable broadcasting over channels |
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for the purpose of making it easier to understand- can we split the computation of memory matrix into memory updates and only then sum them in the separate line? Implementation looks correct btw!
momentfm/utils/t5_infini.py
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| z = sigma_k.sum(dim=-2).unsqueeze(-1).sum(dim=1) # [batch_size, n_heads, dim, 1] sum over sequence length and channels | ||
| z = z.unsqueeze(dim=1) # [batch_size, 1, n_heads, dim, 1] | ||
| sigma_q = self.elu(query_states) + 1.0 # [batch_size, n_series, n_heads, n_patch, dim] | ||
| A_mem = (sigma_q @ memory_matrix) / ((sigma_q @ z) + 1e-6) # [batch_size, n_series, n_heads, n_patch, dim]/[batch_size, n_series, n_heads, n_patch, 1] --> [batch_size, n_series, n_heads, n_patch, dim] Adding 1e-6 for preventing division to 0 |
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