complete speculative sampling with prophet net on cached embeddings i…

…dea!

README.md

@@ -17,11 +17,9 @@ Also have a few ideas of my own that I will try and share in this repository, if
 - [x] figure out batched spec decoding - different rows may advance at different rates
 - [x] further optimize batched spec decoding, as losing some performance from all the indexing - seems like it will take some work for this technique to be actually usable
 - [x] make batched spec decoding work with early exit strategy
+- [x] complete speculative sampling with prophet transformer idea - seems to work well! 🙌
 
-- [ ] build out the prophet net idea, but use the same scheme as megabyte, the hierarchical transformer, for the prophet head. this hierarchical transformer would then use the cached embedding from the large model (since we are caching the embeddings)
-    - [x] complete prophet net with hierarchical transformer training
-    - [ ] complete the spec decoding algorithm using trained prophet net transformer
-
+- [ ] get some wandb charts and see how prophet compares with early exit strategy, share on repository
 - [ ] for early exit strategy, try randomly summing last cached embedding back to the same model (a la alphafold2 recycling), randomly cropped along sequence length, and train early exit loss this way. see if one can improve the gamma this way
 - [ ] dedicate a morning to microoptimizations
 

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'speculative-decoding',
   packages = find_packages(exclude=[]),
-  version = '0.0.16',
+  version = '0.1.0',
   license='MIT',
   description = 'Speculative Decoding',
   author = 'Phil Wang',

speculative_decoding/speculative_decoding_with_prophet.py

@@ -92,171 +92,6 @@ def base_decoding(
 
     return out[..., prompt_seq_len:]
 
-# speculative decoding functions
-
-def safe_div(num, den, eps = 1e-10):
-    return num / max(den, eps)
-
-def find_first_true_index(bool_tensor, dim = -1):
-    return (bool_tensor.cumsum(dim = dim) == 0).sum(dim = dim)
-
-@torch.no_grad()
-def speculative_decoding_with_prophet_model(
-    net: Module,
-    prompt: Tensor,
-    seq_len: int,
-    gamma: int = 5,
-    temperature = 1.,
-    filter_thres = 0.9,
-    lenience = 1.,
-    pad_id = 0
-):
-    """
-    eq. algorithm 1 in paper https://arxiv.org/abs/2211.17192
-    """
-
-    raise NotImplementedError
-
-    batch, prompt_seq_len, out, device = *prompt.shape, prompt.clone(), prompt.device
-    sample_num_times = max(0, seq_len - prompt_seq_len)
-
-    cache = None
-    small_cache = None
-
-    num_steps = 0
-    total_accepted = 0
-
-    batch_range = torch.arange(batch, device = device, dtype = torch.long)[..., None]
-    seq_lens = torch.full((batch,), prompt_seq_len, device = device, dtype = torch.long)
-
-    while (seq_lens < seq_len).any():
-
-        # predict with smaller network
-
-        all_small_logits = []
-        q_sampled_out = []
-
-        for _ in range(gamma):
-            small_logits, small_cache = net(
-                out,
-                cache = small_cache,
-                return_cache = True,
-                return_early_exit_only = True,
-                seq_start_pos = out.shape[-1] - seq_lens
-            )
-
-            small_logits = small_logits[:, -1]
-
-            small_logits = top_k(small_logits, thres = filter_thres)
-            all_small_logits.append(small_logits)
-
-            sample = gumbel_sample(small_logits, temperature = temperature, dim = -1)
-            out = torch.cat((out, sample[..., None]), dim = -1)
-            seq_lens += 1
-
-            q_sampled_out.append(rearrange(sample, 'b -> b 1 1'))
-
-        q_sampled_out = torch.cat(q_sampled_out, dim = -2)
-        small_logits = torch.stack(all_small_logits, dim = -2)
-
-        # verify with larger network
-
-        logits, cache = net(
-            out,
-            cache = cache,
-            early_exit_cache = small_cache,
-            return_cache = True,
-            start_from_early_exit_hiddens = True,
-            seq_start_pos = out.shape[-1] - seq_lens
-        )
-
-        logits = logits[..., -(gamma + 1):, :]
-        logits = top_k(logits, thres = filter_thres)
-
-        # prob and prob of small model (p(x) and q(x) in algorithm 1)
-
-        prob = safe_div(logits, temperature).softmax(dim = -1)
-        small_prob = safe_div(small_logits, temperature).softmax(dim = -1)
-
-        p, prob_next = prob[:, :-1], prob[:, -1]
-
-        p = p.gather(-1, q_sampled_out)
-        q = small_prob.gather(-1, q_sampled_out) * lenience
-
-        p, q = [rearrange(t, 'b n 1 -> b n') for t in (p, q)]
-
-        r = random_uniform = torch.zeros_like(q).float().uniform_(0, 1)
-
-        accepted = find_first_true_index(r > (p / q))
-
-        total_accepted += accepted.float().mean()
-        num_steps += 1
-
-        num_rejected = gamma - accepted
-        has_rejected = num_rejected > 0
-
-        accepted = rearrange(accepted, 'b -> b 1')
-        accepted.clamp_(max = gamma - 1)
-
-        adjusted_prob = F.relu(prob[batch_range, accepted] - small_prob[batch_range, accepted])
-        adjusted_prob = adjusted_prob / adjusted_prob.sum(dim = -1, keepdim = True)
-        adjusted_prob = rearrange(adjusted_prob, 'b 1 d -> b d')
-
-        prob_next = torch.where(
-            rearrange(has_rejected, '... -> ... 1'),
-            adjusted_prob,
-            prob_next
-        )
-
-        # do a bunch of slicing and align everything to the right, including kv caches
-
-        max_num_rejected = num_rejected.amax()
-        seq_arange = torch.arange(out.shape[-1], device = device, dtype = torch.long)
-        seq_offset_indices = seq_arange + (max_num_rejected - num_rejected)[..., None]
-
-        seq_lens -= num_rejected
-        max_seq_len = seq_lens.amax()
-
-        if batch > 1:
-            out = F.pad(out, (0, max_num_rejected), value = pad_id)
-            out = out[batch_range, seq_offset_indices]
-
-            cache = tuple(F.pad(t, (0, 0, 0, max_num_rejected), value = pad_id) for t in cache)
-            small_cache = tuple(F.pad(t, (0, 0, 0, max_num_rejected), value = pad_id) for t in small_cache)
-
-            cache = tuple(rearrange(t, 'b ... n d -> b n ... d') for t in cache)
-            small_cache = tuple(rearrange(t, 'b ... n d -> b n ... d') for t in small_cache)
-
-            cache = tuple(t[batch_range, seq_offset_indices] for t in cache)
-            small_cache = tuple(t[batch_range, seq_offset_indices] for t in small_cache)
-
-            cache = tuple(rearrange(t, 'b n ... d -> b ... n d') for t in cache)
-            small_cache = tuple(rearrange(t, 'b n ... d -> b ... n d') for t in small_cache)
-
-            if out.shape[-1] > max_seq_len:
-                left_index = out.shape[-1] - max_seq_len
-                out = out[:, left_index:]
-                cache = tuple(t[..., left_index:, :] for t in cache)
-                small_cache = tuple(t[..., left_index:, :] for t in small_cache)
-
-        # sample the additional token, one of the tricks in the paper to better bound the worst case
-
-        next_token = torch.multinomial(prob_next, 1)
-
-        out = torch.cat((out, next_token), dim = -1)
-        seq_lens += 1
-
-    # now left align
-
-    num_pad_left = out.shape[-1] - seq_lens
-    max_pad_left = num_pad_left.amax()
-    out = F.pad(out, (0, max_pad_left), value = pad_id)
-
-    seq_len_range = torch.arange(seq_len, device = device, dtype = torch.long)
-    out = out[batch_range, seq_len_range + num_pad_left[..., None]]
-
-    return out[..., prompt_seq_len:], total_accepted / num_steps
-
 # norm
 
 class RMSNorm(Module):
@@ -394,6 +229,9 @@ def forward(
         x = self.token_emb(x)
 
         if exists(start_tokens):
+            if start_tokens.ndim == 2:
+                start_tokens = rearrange(start_tokens, 'b d -> b 1 d')
+
             x = torch.cat((start_tokens, x), dim = 1)
 
         # handle seq start pos offset
@@ -504,10 +342,192 @@ def forward(self, x):
 
         prophet_input = rearrange(prophet_input, '... n -> (...) n')
 
-        prophet_start_tokens = rearrange(prophet_start_tokens[:, :num_seq_train_prophet], 'b n d -> (b n) 1 d')
+        prophet_start_tokens = rearrange(prophet_start_tokens[:, :num_seq_train_prophet], 'b n d -> (b n) d')
 
         prophet_loss, _ = self.prophet(prophet_input, start_tokens = prophet_start_tokens, return_loss = True)
 
         total_loss = total_loss + prophet_loss
 
         return total_loss, (main_loss, prophet_loss)
+
+# speculative decoding functions
+
+def safe_div(num, den, eps = 1e-10):
+    return num / max(den, eps)
+
+def find_first_true_index(bool_tensor, dim = -1):
+    return (bool_tensor.cumsum(dim = dim) == 0).sum(dim = dim)
+
+@torch.no_grad()
+def speculative_decoding_with_prophet_model(
+    net: ModelWithProphetWrapper,
+    prompt: Tensor,
+    seq_len: int,
+    gamma: int = 5,
+    temperature = 1.,
+    filter_thres = 0.9,
+    lenience = 1.,
+    pad_id = 0
+):
+    """
+    eq. algorithm 1 in paper https://arxiv.org/abs/2211.17192
+    """
+
+    # extract model, prophet, and model to prophet (if their model dimensions differ)
+
+    model = net.model
+    to_prophet_start_token = net.to_prophet_start_token
+    prophet = net.prophet
+
+    batch, prompt_seq_len, out, device = *prompt.shape, prompt.clone(), prompt.device
+
+    if (seq_len - prompt_seq_len) <= 0:
+        return prompt, None
+
+    cache = None
+    small_cache = None
+
+    num_steps = 0
+    total_accepted = 0
+
+    batch_range = torch.arange(batch, device = device, dtype = torch.long)[..., None]
+    seq_lens = torch.full((batch,), prompt_seq_len, device = device, dtype = torch.long)
+
+    # sample the first token from the main model
+
+    logits, cache = model(out, return_cache = True)
+    logits = logits[:, -1:]
+    logits = top_k(logits, thres = filter_thres)
+    sample = gumbel_sample(logits, temperature = temperature, dim = -1)
+    out = torch.cat((out, sample), dim = -1)
+    seq_lens += 1
+
+    # now we have the first cached embedding to use as the prophet network start token for the speculative sampling
+
+    _, embeds = cache
+    next_prophet_start_tokens = to_prophet_start_token(embeds[:, -1])
+
+    while (seq_lens < seq_len).any():
+
+        # predict with smaller network
+
+        all_small_logits = []
+        q_sampled_out = []
+
+        small_cache = None
+        num_tokens = 2  # the main model embeddings is 1 step behind the main sequence
+
+        for _ in range(gamma):
+            small_logits, small_cache = prophet(
+                out[..., -num_tokens:],
+                start_tokens = next_prophet_start_tokens,
+                cache = small_cache,
+                return_cache = True
+            )
+
+            small_logits = small_logits[:, -1:]
+
+            small_logits = top_k(small_logits, thres = filter_thres)
+            all_small_logits.append(small_logits)
+
+            sample = gumbel_sample(small_logits, temperature = temperature, dim = -1)
+            out = torch.cat((out, sample), dim = -1)
+
+            seq_lens += 1
+            num_tokens += 1
+
+            q_sampled_out.append(rearrange(sample, '... -> ... 1'))
+
+        q_sampled_out = torch.cat(q_sampled_out, dim = -2)
+        small_logits = torch.cat(all_small_logits, dim = -2)
+
+        # verify with larger network
+
+        logits, cache = model(
+            out,
+            cache = cache,
+            return_cache = True,
+            seq_start_pos = out.shape[-1] - seq_lens
+        )
+
+        logits = logits[..., -(gamma + 1):, :]
+        logits = top_k(logits, thres = filter_thres)
+
+        # prob and prob of small model (p(x) and q(x) in algorithm 1)
+
+        prob = safe_div(logits, temperature).softmax(dim = -1)
+        small_prob = safe_div(small_logits, temperature).softmax(dim = -1)
+
+        p, prob_next = prob[:, :-1], prob[:, -1]
+
+        p = p.gather(-1, q_sampled_out)
+        q = small_prob.gather(-1, q_sampled_out) * lenience
+
+        p, q = [rearrange(t, 'b n 1 -> b n') for t in (p, q)]
+
+        r = random_uniform = torch.zeros_like(q).float().uniform_(0, 1)
+
+        accepted = find_first_true_index(r > (p / q))
+
+        total_accepted += accepted.float().mean()
+        num_steps += 1
+
+        num_rejected = gamma - accepted
+        has_rejected = num_rejected > 0
+
+        accepted = rearrange(accepted, 'b -> b 1')
+        accepted.clamp_(max = gamma - 1)
+
+        adjusted_prob = F.relu(prob[batch_range, accepted] - small_prob[batch_range, accepted])
+        adjusted_prob = adjusted_prob / adjusted_prob.sum(dim = -1, keepdim = True)
+        adjusted_prob = rearrange(adjusted_prob, 'b 1 d -> b d')
+
+        prob_next = torch.where(
+            rearrange(has_rejected, '... -> ... 1'),
+            adjusted_prob,
+            prob_next
+        )
+
+        # do a bunch of slicing and align everything to the right, including kv caches
+
+        max_num_rejected = num_rejected.amax()
+        seq_arange = torch.arange(out.shape[-1], device = device, dtype = torch.long)
+        seq_offset_indices = seq_arange + (max_num_rejected - num_rejected)[..., None]
+
+        seq_lens -= num_rejected
+        max_seq_len = seq_lens.amax()
+
+        if batch > 1:
+            out = F.pad(out, (0, max_num_rejected), value = pad_id)
+            out = out[batch_range, seq_offset_indices]
+
+            cache = tuple(F.pad(t, (0, 0, 0, max_num_rejected), value = pad_id) for t in cache)
+            cache = tuple(rearrange(t, 'b ... n d -> b n ... d') for t in cache)
+            cache = tuple(t[batch_range, seq_offset_indices] for t in cache)
+            cache = tuple(rearrange(t, 'b n ... d -> b ... n d') for t in cache)
+
+            if out.shape[-1] > max_seq_len:
+                left_index = out.shape[-1] - max_seq_len
+                out = out[:, left_index:]
+                cache = tuple(t[..., left_index:, :] for t in cache)
+
+        # sample the additional token, one of the tricks in the paper to better bound the worst case
+
+        next_token = torch.multinomial(prob_next, 1)
+
+        out = torch.cat((out, next_token), dim = -1)
+        seq_lens += 1
+
+        _, embeds = cache
+        next_prophet_start_tokens = to_prophet_start_token(embeds[:, -1])
+
+    # now left align
+
+    num_pad_left = out.shape[-1] - seq_lens
+    max_pad_left = num_pad_left.amax()
+    out = F.pad(out, (0, max_pad_left), value = pad_id)
+
+    seq_len_range = torch.arange(seq_len, device = device, dtype = torch.long)
+    out = out[batch_range, seq_len_range + num_pad_left[..., None]]
+
+    return out[..., prompt_seq_len:], total_accepted / num_steps