Dynamic shapes: use t.size(d) output as input to .view/.reshape

LitGPT's CausalSelfAttention.forward contains the following code, which uses sizes (in particular batch size and sequence length in inputs). It would be super useful if dynamic shape support meant that we could get around re-acquiring the Python for varying batch size (so we would need support for .size returning tuples on NumberProxy, possible very simple computation on them (but not here, I think), and then feeding them into .view and .reshape.

       B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)

        qkv = self.attn(x)

        # assemble into a number of query groups to support MHA, MQA and GQA together (see `config.n_query_groups`)
        q_per_kv = self.config.n_head // self.config.n_query_groups
        total_qkv = q_per_kv + 2  # each group has 1+ queries, 1 key, and 1 value
        qkv = qkv.view(B, T, self.config.n_query_groups, total_qkv, self.config.head_size)
        qkv = qkv.permute(0, 2, 3, 1, 4)  # (B, n_query_groups, total_qkv, T, hs)

        # split batched computation into three
        q, k, v = qkv.split((q_per_kv, 1, 1), dim=2)
       ...
        q = q.reshape(B, -1, T, self.config.head_size)  # (B, nh_q, T, hs)
        k = k.reshape(B, -1, T, self.config.head_size)  # (B, nh_k, T, hs)
        v = v.reshape(B, -1, T, self.config.head_size)  # (B, nh_v, T, hs)

        ...

        y = y.reshape(B, T, self.config.head_size * self.config.n_head)  # re-assemble all head outputs side by side

        # output projection
        return self.proj(y)

@jjsjann123