From 884fadc7441d09eadedde1d0e3d8df36fb505b0d Mon Sep 17 00:00:00 2001 From: Daniel Han-Chen Date: Sun, 25 Feb 2024 04:14:57 +1100 Subject: [PATCH] Update gemma.py --- unsloth/models/gemma.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/unsloth/models/gemma.py b/unsloth/models/gemma.py index a2c2e0893d..8d40a63b66 100644 --- a/unsloth/models/gemma.py +++ b/unsloth/models/gemma.py @@ -76,17 +76,13 @@ class FastGemmaRotaryEmbedding(torch.nn.Module): # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and # in FP32. They are applied (multiplied) in FP32 as well. self.max_seq_len_cached = max(self.max_position_embeddings, seq_len) - print(self.max_seq_len_cached) inv_freq = 1.0 / ( self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device="cpu").float() / self.dim) ) t = torch.arange(self.max_position_embeddings, device="cpu", dtype=torch.int64).float().to("cuda").unsqueeze(0) inv_freq_expanded = inv_freq[None, :, None].float().expand(1, -1, 1).to("cuda") position_ids_expanded = t[:, None, :].float() - print(position_ids_expanded.shape, position_ids_expanded.to(torch.float64)) - print(inv_freq_expanded.shape, inv_freq_expanded.to(torch.float64)) freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2) - print(freqs.to(torch.int64)) emb = torch.cat((freqs, freqs), dim=-1) self.cos_cached = emb.cos().to(dtype=torch.bfloat16) @@ -107,12 +103,10 @@ class FastGemmaRotaryEmbedding(torch.nn.Module): self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device="cuda").float() / self.dim) ) - print(self.max_position_embeddings) t = torch.arange(self.max_position_embeddings, device="cpu", dtype=torch.int64).float().to("cuda").unsqueeze(0) inv_freq_expanded = self.inv_freq[None, :, None].float().expand(1, -1, 1).to("cuda") position_ids_expanded = t[:, None, :].float() freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2) - print(freqs.to(torch.int64)) emb = torch.cat((freqs, freqs), dim=-1) seq_len = position_ids.shape[1]