Sketch a quantized llama using the pyo3 api. (#715)

* Sketch a quantized llama using the pyo3 api.

* Add more ops.

* Expose a few more functions to use in the quantized model.

* Rope embeddings.

* Get the forward pass to work.

1 files changed, 171 insertions, 0 deletions

diff --git a/candle-pyo3/quant-llama.py b/candle-pyo3/quant-llama.py
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+++ b/candle-pyo3/quant-llama.py
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+# This example shows how the candle Python api can be used to replicate llama.cpp.
+import os
+import sys
+
+# The "import candle" statement below works if there is a "candle.so" file in sys.path.
+# Here we check for shared libraries that can be used in the build directory.
+BUILD_DIR = "./target/release-with-debug"
+so_file = BUILD_DIR + "/candle.so"
+if os.path.islink(so_file): os.remove(so_file)
+for lib_file in ["libcandle.dylib", "libcandle.so"]:
+    lib_file_ = BUILD_DIR + "/" + lib_file
+    if os.path.isfile(lib_file_):
+        os.symlink(lib_file, so_file)
+        sys.path.insert(0, BUILD_DIR)
+        break
+
+import candle
+
+MAX_SEQ_LEN = 4096
+
+def masked_fill(on_false, mask, on_true):
+    shape = mask.shape
+    on_true = candle.tensor(on_true).broadcast_as(shape)
+    return mask.where_cond(on_true, on_false)
+
+class RmsNorm:
+    def __init__(self, qtensor):
+        self.weight = qtensor.dequantize()
+
+    def __call__(self, x):
+        b_size, seq_len, hidden_size = x.shape
+        norm_x = x.sqr().sum_keepdim(2) / hidden_size
+        x_normed = x.broadcast_div((norm_x + 1e-5).sqrt())
+        return x_normed.broadcast_mul(self.weight)
+
+class QuantizedLayer:
+    def __init__(self, layer_idx, hparams, all_tensors, cos_sin):
+        p = f"layers.{layer_idx}"
+        self.attention_wq = all_tensors[f"{p}.attention.wq.weight"]
+        self.attention_wk = all_tensors[f"{p}.attention.wk.weight"]
+        self.attention_wv = all_tensors[f"{p}.attention.wv.weight"]
+        self.attention_wo = all_tensors[f"{p}.attention.wo.weight"]
+        self.ffw1 = all_tensors[f"{p}.feed_forward.w1.weight"]
+        self.ffw2 = all_tensors[f"{p}.feed_forward.w2.weight"]
+        self.ffw3 = all_tensors[f"{p}.feed_forward.w3.weight"]
+        self.attn_norm = RmsNorm(all_tensors[f"{p}.attention_norm.weight"])
+        self.ffn_norm = RmsNorm(all_tensors[f"{p}.ffn_norm.weight"])
+
+        self.n_head = hparams["n_head"]
+        self.n_kv_head = self.n_head
+        self.head_dim = hparams["n_embd"] // self.n_head
+
+        self.kv_cache = None
+        self.cos = cos_sin[0]
+        self.sin = cos_sin[1]
+
+    def __call__(self, x, mask, index_pos):
+        residual = x
+        x = self.attn_norm(x)
+        attn = self.forward_attn(x, mask, index_pos) 
+        x = attn + residual
+
+        residual = x
+        x = self.ffn_norm(x)
+        w1 = self.ffw1.matmul_t(x)
+        w3 = self.ffw3.matmul_t(x)
+        mlp = self.ffw2.matmul_t(candle.nn.silu(w1) * w3)
+
+        return mlp + residual
+
+    def forward_attn(self, x, mask, index_pos):
+        b_size, seq_len, n_embd = x.shape
+        q = self.attention_wq.matmul_t(x)
+        k = self.attention_wk.matmul_t(x)
+        v = self.attention_wv.matmul_t(x)
+
+        q = q.reshape((b_size, seq_len, self.n_head, self.head_dim)).transpose(1, 2)
+        k = k.reshape((b_size, seq_len, self.n_kv_head, self.head_dim)).transpose(1, 2)
+        v = v.reshape((b_size, seq_len, self.n_kv_head, self.head_dim)).transpose(1, 2)
+
+        q = self.apply_rotary_emb(q, index_pos)
+        k = self.apply_rotary_emb(k, index_pos)
+
+        if self.kv_cache is not None and index_pos > 0:
+            prev_k, prev_v = self.kv_cache
+            k = candle.cat([prev_k, k], 2).contiguous()
+            v = candle.cat([prev_v, v], 2).contiguous()
+
+        self.kv_cache = (k, v)
+
+        # TODO: maybe repeat k/v here if we start supporting MQA.
+
+        att = q.matmul(k.t()) / self.head_dim**0.5
+        mask = mask.broadcast_as(att.shape)
+        att = masked_fill(att, mask, float("-inf"))
+        att = candle.nn.softmax(att, -1)
+        y = att.matmul(v.contiguous())
+        y = y.transpose(1, 2).reshape((b_size, seq_len, n_embd))
+        return self.attention_wo.matmul_t(y)
+
+    def apply_rotary_emb(self, x, index_pos):
+        (b_size, n_head, seq_len, n_embd) = x.shape
+        cos = self.cos.narrow(0, index_pos, seq_len).reshape((seq_len, n_embd//2, 1))
+        sin = self.sin.narrow(0, index_pos, seq_len).reshape((seq_len, n_embd//2, 1))
+        x = x.reshape((b_size, n_head, seq_len, n_embd//2, 2))
+        x0 = x.narrow(-1, 0, 1)
+        x1 = x.narrow(-1, 1, 1)
+        y0 = x0.broadcast_mul(cos) - x1.broadcast_mul(sin)
+        y1 = x0.broadcast_mul(sin) + x1.broadcast_mul(cos)
+        rope = candle.cat([y0, y1], -1)
+        return rope.flatten_from(-2)
+
+def precompute_freqs_cis(hparams, freq_base):
+    head_dim = hparams["n_embd"] // hparams["n_head"]
+    theta = [1.0 / freq_base ** (i / head_dim) for i in range(0, head_dim, 2)]
+    theta = candle.tensor(theta)
+    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:
+    def __init__(self, hparams, all_tensors):
+        self.tok_embeddings = all_tensors["tok_embeddings.weight"].dequantize()
+        self.norm = RmsNorm(all_tensors["norm.weight"])
+        self.output = all_tensors["output.weight"]
+        self.layers = []
+        cos_sin = precompute_freqs_cis(hparams, 10000.)
+        for layer_idx in range(hparams["n_layer"]):
+            layer = QuantizedLayer(layer_idx, hparams, all_tensors, cos_sin)
+            self.layers.append(layer)
+
+    def __call__(self, token, index_pos):
+        b_size, seq_len = token.shape
+        vocab_size, hidden_size = self.tok_embeddings.shape
+        token = token.reshape((b_size * seq_len,))
+        x = self.tok_embeddings.index_select(token, 0)
+        x = x.reshape((b_size, seq_len, hidden_size))
+
+        mask = [int(j > i) for j in range(seq_len) for i in range(seq_len)]
+        mask = candle.tensor(mask).reshape((seq_len, seq_len))
+
+        for layer in self.layers:
+            x = layer(x, mask, index_pos)
+        return x
+
+def main():
+    if len(sys.argv) < 2:
+        raise ValueError("missing weight file argument")
+    filename = sys.argv[1]
+    if filename.endswith("gguf"):
+        all_tensors = candle.load_gguf(sys.argv[1])
+        hparams = None
+    else:
+        all_tensors, hparams = candle.load_ggml(sys.argv[1])
+    print(hparams)
+    model = QuantizedLlama(hparams, all_tensors)
+
+    tokens = [1]
+    for token_idx in range(1):
+        print(tokens)
+        last_token = tokens[-1]
+        lt = candle.tensor([last_token]).unsqueeze(0)
+        logits = model(lt, len(tokens))
+        print(logits)
+        next_token = "TODO: sample"
+        tokens.append(next_token)
+
+if __name__ == '__main__':
+    main()