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egs/librispeech/ASR/pruned_transducer_stateless5/sampling.py

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+#!/usr/bin/env python3
+
+# This was copied from /ceph-dan/torch-sampling/torch_sampling/sampling_ref.py,
+# its git history is there.
+
+import timeit
+import torch
+from torch import Tensor
+from torch import nn
+from torch.cuda.amp import GradScaler, custom_fwd, custom_bwd
+from typing import Tuple, Optional
+from scaling import ScaledLinear
+import random
+from torch_scheduled_sampling import sample_combined
+
+# The main exports of this file are the module KnowledgeBaseLookup and the
+# function create_knowledge_base.
+
+
+
+
+
+
+def create_knowledge_base(M: int, N: int, D: int) -> nn.Parameter:
+    std = 0.1
+    a = (3 ** 0.5) * std  # this sqrt(3) thing is intended to get variance of
+                          # 0.1 from uniform distribution
+    ans = nn.Parameter(torch.ones(M ** N, D))
+    nn.init.uniform_(ans, -a, a)
+    return ans
+
+def join_indexes(indexes: Tensor, M: int) -> Tensor:
+    """
+    Combines N-tuples of indexes into single indexes that can be used for
+    lookup in the knowledge base.  Args:
+      indexes: tensor of torch.int64 of shape (*, K, N), with elements in
+         {0..M-1}
+         M: the size of the original softmaxes, is upper bound on elements
+           in indexes
+       Returns:
+          joined_indexes: of shape (*, K), joined_indexes[...,k] equals
+            joined_indexes[...,0,k] + joined_indexes[...,1,k]*(M**1) ... + joined_indexes[...,1,k]*(M**(N-1))]
+    """
+    N = indexes.shape[-1]
+    n_powers = M ** torch.arange(N, device=indexes.device)  # [ 1, M, ..., M**(N-1) ]
+    return (indexes * n_powers).sum(dim=-1)
+
+
+# Note, we don't use this, we
+def weighted_matrix_lookup(weights: Tensor,
+                           indexes: Tensor,
+                           knowledge_base: Tensor) -> Tensor:
+    """
+    Weighted combination of specified rows of a matrix.
+         weights: Tensor of shape (*, K), can contain any value but probably in [0..1].
+         indexes: Tensor of shape (*, K), with elements in [0..C-1]
+         knowledge_base: Tensor of shape (C-1, D), whose rows we'll be looking up
+      Returns:
+         tensor of shape (*, D), containing weighted sums of rows of
+         `knowledge_base`
+    """
+    if True:
+        return WeightedMatrixLookupFunction.apply(weights, indexes, knowledge_base)
+    else:
+        # simpler but less memory-efficient implementation
+        lookup = torch.index_select(knowledge_base, dim=0, index=indexes.flatten())
+        D = knowledge_base.shape[-1]
+        weights = weights.unsqueeze(-2)   # (*, 1, K)
+        lookup = lookup.reshape(*indexes.shape, D) # (*, K, D)
+        ans = torch.matmul(weights, lookup) # ans: (*, 1, D)
+        ans = ans.squeeze(-2)
+        assert list(ans.shape) == list(weights.shape[:-2]) + [D]
+        return ans
+
+
+class WeightedMatrixLookupFunction(torch.autograd.Function):
+    @staticmethod
+    @custom_fwd
+    def forward(ctx, weights: Tensor, indexes: Tensor, knowledge_base: Tensor) -> Tensor:
+        """
+        Weighted combination of specified rows of a matrix.
+         weights: Tensor of shape (*, K), can contain any value but probably in [0..1].
+         indexes: Tensor of shape (*, K), with elements in [0..C-1]
+         knowledge_base: Tensor of shape (C, D), whose rows we'll be looking up
+        Returns:
+         tensor of shape (*, D), containing weighted sums of rows of
+         `knowledge_base`
+        """
+        if random.random() < 0.001:
+            print("dtype[1] = ", weights.dtype)
+        ctx.save_for_backward(weights.detach(), indexes.detach(),
+                              knowledge_base.detach())
+        with torch.no_grad():
+            lookup = torch.index_select(knowledge_base, dim=0, index=indexes.flatten())
+            D = knowledge_base.shape[-1]
+            weights = weights.unsqueeze(-2)   # (*, 1, K)
+            lookup = lookup.reshape(*indexes.shape, D) # (*, K, D)
+            ans = torch.matmul(weights, lookup) # ans: (*, 1, D)
+            ans = ans.squeeze(-2) #(*, D)
+        return ans
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None, Tensor]:
+        # ans_grad: (*, D)
+        weights, indexes, knowledge_base = ctx.saved_tensors
+        knowledge_base.requires_grad = True
+        dtype = ans_grad.dtype
+        ans_grad = ans_grad.to(weights.dtype)
+        assert weights.requires_grad == False
+        D = knowledge_base.shape[-1]
+        with torch.enable_grad():
+            # we'll use torch's autograd to differentiate this operation, which
+            # is nontrivial [and anyway we need `lookup` to compute weight grad.
+            # We don't save `lookup` because it's large, that is the reason
+            # we override Torch autograd.
+            lookup = torch.index_select(knowledge_base, dim=0, index=indexes.flatten())
+            lookup = lookup.reshape(*indexes.shape, D) # (*, K, D)
+        weights = weights.unsqueeze(-1)   # (*, K, 1)
+        # forward pass: was:
+        ## ans = torch.matmul(weights, lookup)
+        ## ans: (*, 1, D)
+        ## ans = ans.squeeze(-2) # ans, ans_grad: (*, D)
+        weights_grad = torch.matmul(lookup,  # (*, K, D)
+                                    ans_grad.unsqueeze(-1))  # (*, D, 1)
+        weights_grad = weights_grad.squeeze(-1) # (*, K, 1) -> (*, K)
+        lookup_grad = weights * ans_grad.unsqueeze(-2) # (*, K, 1) * (*, 1, D) = (*, K, D)
+        lookup.backward(gradient=lookup_grad)
+        return weights_grad.to(dtype), None, knowledge_base.grad.to(dtype)
+
+
+class KnowledgeBaseLookup(nn.Module):
+    """
+    Create knowledge-base lookup module.  (The knowledge-base parameter, which is
+    large, is shared between these modules).
+    Args:
+       M: int, softmax size, e.g. in [32..128]
+       N: int, number of softmaxes, in [2..3]
+       D: int, embedding dimension in knowledge base, e.g. 256
+       K: number of samples (affects speed/accuracy tradeoff), e.g. 16.
+      embedding_dim:  the dimension to project from and to, e.g. the
+        d_model of the conformer.
+    """
+    def __init__(self, M: int, N: int, D: int,
+                 K: int, embedding_dim: int,
+                 knowledge_base: nn.Parameter):
+        super(KnowledgeBaseLookup, self).__init__()
+        self.knowledge_base = knowledge_base  # shared!
+        self.in_proj = ScaledLinear(embedding_dim, M * N,
+                                    initial_scale=1.0)
+        # initial_scale = 4.0 because the knowlege_base activations are
+        # quite small -- if we use our optimizer they'll have stddev <= 0.1.
+        self.out_proj = ScaledLinear(D, embedding_dim,
+                                     initial_scale = 4.0)
+        self.M = M
+        self.N = N
+        self.K = K
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Forward function that does knowledge-base lookup.
+        Args:
+             x: input, of shape (*, E) where E is embedding_dim
+                as passed to constructor
+             y: output of knowledge-base lookup, of shape (*, E)
+
+        # TODO: later we can try multiplying by a projection of x or something like that.
+        """
+        assert torch.all(x - x == 0)
+        x = self.in_proj(x) # now (*, M*N)
+        assert torch.all(x - x == 0)
+        x = x.reshape(*x.shape[:-1], self.N, self.M) # now (*, N, M)
+        x = x.log_softmax(dim=-1) # now normalized logprobs, dim= (*, N, M)
+        assert torch.all(x - x == 0)
+        if random.random() < 0.001:
+            entropy = (x * x.exp()).sum(dim=-1).mean()
+            print("Entropy = ", entropy)
+        # only need 'combined_indexes', call them 'indexes'.
+        _, indexes, weights = sample_combined(x, self.K, input_is_log=True)
+        x = weighted_matrix_lookup(weights, indexes, self.knowledge_base) # now (*, D)
+        x = self.out_proj(x) # now (*, self.embedding_dim)
+        return x
+
+
+def _test_knowledge_base_lookup():
+    K = 16
+    N = 2
+    M = 128
+    D = 256
+    E = 255
+
+    knowledge_base: nn.Parameter = create_knowledge_base(M, N, D)
+    m = KnowledgeBaseLookup(M, N, D, K, E, knowledge_base)
+
+    B = 30
+    T = 40
+    x = torch.randn(B, T, E)
+    x.requires_grad = True
+    y = m(x)
+    assert y.shape == x.shape
+    y.sum().backward() # make sure backward doesn't crash..
+    print("y = ", y)
+    print("x.grad = ", x.grad)
+    print("knowlege_base.grad norm = ", knowledge_base.grad.norm())
+
+    dtype = torch.float32
+    device = torch.device('cuda')
+    train_pairs = [ (torch.randn(B, T, E, device=device, dtype=dtype),  torch.randn(B, T, E, device=device, dtype=dtype)) for _ in range(10) ]
+    from optim import Eve
+    optimizer = Eve(m.parameters(), lr=0.005, eps=1.0e-04)
+    m = m.to(device).to(dtype)
+
+
+    start = timeit.default_timer()
+
+# Epoch 0, batch 0, loss 1.0109944343566895
+# Epoch 10, batch 0, loss 1.0146660804748535
+# Epoch 20, batch 0, loss 1.0119813680648804
+# Epoch 30, batch 0, loss 1.0105408430099487
+# Epoch 40, batch 0, loss 1.0077732801437378
+# Epoch 50, batch 0, loss 1.0050103664398193
+# Epoch 60, batch 0, loss 1.0033129453659058
+# Epoch 70, batch 0, loss 1.0014232397079468
+# Epoch 80, batch 0, loss 0.9977912306785583
+# Epoch 90, batch 0, loss 0.8274348974227905
+# Epoch 100, batch 0, loss 0.3368612825870514
+# Epoch 110, batch 0, loss 0.11323091387748718
+# Time taken:  17.591704960912466
+    for epoch in range(150):
+        for n, (x,y) in enumerate(train_pairs):
+            y_out = m(x)
+            loss = ((y_out - y)**2).mean() * 100.0
+            if n % 10 == 0 and epoch % 10 == 0:
+                print(f"Epoch {epoch}, batch {n}, loss {loss.item()}")
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+
+    stop = timeit.default_timer()
+    print('Time taken: ', stop - start)
+
+def _test_knowledge_base_lookup_autocast():
+    K = 16
+    N = 2
+    M = 128
+    D = 256
+    E = 255
+
+    knowledge_base: nn.Parameter = create_knowledge_base(M, N, D)
+    m = KnowledgeBaseLookup(M, N, D, K, E, knowledge_base)
+
+    B = 30
+    T = 40
+    x = torch.randn(B, T, E)
+    x.requires_grad = True
+    y = m(x)
+    assert y.shape == x.shape
+    y.sum().backward() # make sure backward doesn't crash..
+    print("y = ", y)
+    print("x.grad = ", x.grad)
+    print("knowlege_base.grad norm = ", knowledge_base.grad.norm())
+
+    device = torch.device('cuda')
+    train_pairs = [ (torch.randn(B, T, E, device=device),  torch.randn(B, T, E, device=device)) for _ in range(10) ]
+    from optim import Eve
+    optimizer = Eve(m.parameters(), lr=0.005, eps=1.0e-04)
+    m = m.to(device)
+
+    scaler = GradScaler(enabled=True)
+
+    start = timeit.default_timer()
+
+
+    for epoch in range(150):
+        for n, (x,y) in enumerate(train_pairs):
+            y_out = m(x)
+            with torch.cuda.amp.autocast(enabled=True):
+                loss = ((y_out - y)**2).mean() * 100.0
+            if n % 10 == 0 and epoch % 10 == 0:
+                print(f"Epoch {epoch}, batch {n}, loss {loss.item()}")
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+
+    stop = timeit.default_timer()
+    print('Time taken: ', stop - start)
+
+
+
+if __name__ == '__main__':
+    _test_knowledge_base_lookup()
+    _test_knowledge_base_lookup_autocast()