More quantized llama in python. (#716)

* More quantized llama in python.

* Expose a couple more functions.

* Apply the last layer.

* Use the vocab from the ggml files.

candle-pyo3/quant-llama.py

@@ -117,7 +117,6 @@ def precompute_freqs_cis(hparams, freq_base):
     idx_theta = [float(i) for i in range(MAX_SEQ_LEN)]
     idx_theta = candle.tensor(idx_theta).reshape((MAX_SEQ_LEN, 1))
     m = idx_theta.matmul(theta.unsqueeze(0))
-    print(m.shape)
     return (m.cos(), m.sin())
 
 class QuantizedLlama:
@@ -143,28 +142,36 @@ class QuantizedLlama:
 
         for layer in self.layers:
             x = layer(x, mask, index_pos)
+        x = self.norm(x)
+        x = x.narrow(1, -1, 1).squeeze(1)
+        x = self.output.matmul_t(x)
         return x
 
 def main():
     if len(sys.argv) < 2:
         raise ValueError("missing weight file argument")
     filename = sys.argv[1]
+    print(f"reading model file {filename}")
     if filename.endswith("gguf"):
         all_tensors = candle.load_gguf(sys.argv[1])
         hparams = None
+        vocab = None
     else:
-        all_tensors, hparams = candle.load_ggml(sys.argv[1])
+        all_tensors, hparams, vocab = candle.load_ggml(sys.argv[1])
     print(hparams)
     model = QuantizedLlama(hparams, all_tensors)
+    print("model built, starting inference")
 
     tokens = [1]
-    for token_idx in range(1):
-        print(tokens)
+    for token_idx in range(500):
         last_token = tokens[-1]
         lt = candle.tensor([last_token]).unsqueeze(0)
         logits = model(lt, len(tokens))
-        print(logits)
-        next_token = "TODO: sample"
+        # Greedy sampling for now
+        # pr = candle.nn.softmax(logits, -1)
+        m = logits.get(0).argmax_keepdim(-1)
+        next_token = m.values()[0]
+        print(vocab[next_token], end='', flush=True)
         tokens.append(next_token)
 
 if __name__ == '__main__':