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Fixing Dtype error: expected scalar type BFloat16 but found Float for Torch parts #59

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Fixing Dtype error: expected scalar type BFloat16 but found Float for Torch parts #59

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9 changes: 6 additions & 3 deletions entropix/torch_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,14 +47,17 @@ def attention(x: torch.Tensor, layer_weights: LayerWeights, model_params, cur_po
xq = torch.permute(xq, (0, 2, 1, 3)) # (bs, n_heads, seqlen, head_dim)
keys = torch.permute(keys, (0, 2, 3, 1)) # (bs, n_heads, head_dim, cache_len + seqlen)
values = torch.permute(values, (0, 2, 1, 3)) # (bs, n_heads, cache_len + seqlen, head_dim)
xq = xq.to(torch.bfloat16)
keys = keys.to(torch.bfloat16)

scores = torch.matmul(xq, keys)
pre_scores = scores / math.sqrt(model_params.head_dim)
scores = pre_scores.to(torch.float32) # Always do attention softmax at float32
scores = pre_scores.to(torch.bfloat16) # Always do attention softmax at float32
if cur_pos == 0:
scores = scores + attn_mask
mask = torch.where(scores != 0.0, scores, DEFAULT_MASK_VALUE)
padded_logits = torch.where((mask >= DEFAULT_MASK_VALUE * 0.5), scores, DEFAULT_MASK_VALUE)
scores = F.softmax(padded_logits, dim=-1).to(torch.float32)
scores = F.softmax(padded_logits, dim=-1).to(torch.bfloat16)
output = torch.matmul(scores, values)
output = output.transpose(1, 2).reshape(xq.shape[0], xq.shape[2], -1)
out = F.linear(output, layer_weights.wo)
Expand All @@ -77,4 +80,4 @@ def xfmr(xfmr_weights: XfmrWeights, model_params: ModelParams, tokens: torch.Ten
h = h + h_attn
h = h + feed_forward(rms_norm(h, xfmr_weights.layer_weights[i].ffn_norm), xfmr_weights.layer_weights[i])
logits = F.linear(rms_norm(h, xfmr_weights.norm), xfmr_weights.output)
return logits, kvcache, scores, attn_stats
return logits, kvcache, scores, attn_stats