o ۷i'@svdZddlmZddlZddlmZddlmZGdd d ej j j Z Gd d d ej j j Z Gd d d ej j j ZdS)a Generic interface to various configurations of the Perceiver Resampler, that simply takes in a series of (potentially time-indexed) contextual embeddings, and "resamples" (compresses) them down to a pre-specified number of latents! Note that the Perceiver in general resamples based solely off the *long-range* context; there's a nice opportunity here to prime the Perceiver Resampler with say a single layer's worth of language embeddings (the target domain), and use that to softly "retrieve & compress" what we need --> this would be a novel contribution we should explore. References: - DeepMind's Flamingo: https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model - Code borrowed w/ love from: https://github.com/lucidrains/flamingo-pytorch )OptionalN) shape_list) IdeficsConfigcs\eZdZdededededededdffd d Zfd d Zd ejdejfddZ Z S)TFIdeficsPerceiverResamplerconfig embed_dimdepthn_headshead_dim n_latentsreturnNc stjd i|||||f\|_|_|_|_|jj|_t |j ds'|jdn|j jd|_ g|_ t |D]#}|j t|j|j|j|jd|ddt|j |d|ddgq5tjjjddd |_d S) ao Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or MAE) of a given dimension, performs `depth` blocks of cross-attention with a fixed `n_latents` inputs, then returns a Tensor of shape [bsz, n_latents, embed_dim]. :param embed_dim: Dimensionality of embeddings being fed to the Perceiver Resampler (also dimensionality of latent embeddings *returned* by the Perceiver Resampler. Could be e.g., VIT embed_dim, ResNet pool dim, and so on. Args: config (`IdeficsConfig`): config object embed_dim (`int`): The size of each embedding vector depth (`int`): Depth of the Perceiver Resampler (Transformer w/ cross attention). Should be shallow (< 3). n_heads (`int`): Number of heads in each Transformer block (for multi-headed self-attention). head_dim (`int`): Dimensionality of each head projection in the Transformer block. n_latents (`int`): Number of latent embeddings to resample ("compress") the input sequence to (usually < 128). r zblocks.z.0namez.1h㈵> layer_normepsilonrN)super__init__r r r r perceiver_configqk_layer_norms_perceiverqk_layer_normshasattr vision_configintermediate_dimblocksrangeappendTFIdeficsPerceiverAttention TFIdeficsMLPtfkeraslayersLayerNormalizationr) selfrr r r r r kwargsi __class__r^/home/ubuntu/vllm_env/lib/python3.10/site-packages/transformers/models/idefics/perceiver_tf.pyr1s"     z$TFIdeficsPerceiverResampler.__init__cs,|j|j|jfdddd|_t|dS)N random_normalTlatents)shape initializer trainabler) add_weightr r r/rbuild)r( input_shaper+rr-r4\sz!TFIdeficsPerceiverResampler.buildcontextcCs`tj|jdd}t|t|dddg}|jD]\}}||||}|||}q||S)zWResample arbitrary length context & *compress* down to self.n_latents latent embeddingsraxisr)r$ expand_dimsr/tiler0rr)r(r6r/attnffrrr-callcs  z TFIdeficsPerceiverResampler.call) __name__ __module__ __qualname__rintrr4r$Tensorr= __classcell__rrr+r-r0s$ +rc sNeZdZdededededdf fdd Zd ejd ejdejfd d ZZ S) r"r r r rrNc stjdi|||||_|_|_||_tjjj ddd|_ tjjj ddd|_ |jrBtjjj ddd|_ tjjj ddd|_ |jd|_tjjj|j|jdd d |_tjjj|j|jdd d |_tjjj|j|jdd d |_tjjj|dd d |_dS)ziPerceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`rcontext_layer_normrlatents_layer_norm q_layer_norm k_layer_normgFq_projuse_biasrk_projv_proj output_projNr)rrr r r rr$r%r&r'rDrErFrGqk_scaleDenserHrKrLrM)r(r r r rr)r+rr-rps z$TFIdeficsPerceiverAttention.__init__r6r/c s |}|}t|\}}|}tj||gdd}tj||gdd}fdd|||fD\}}}jrK |} |}t d|j |}|tj |ddd} tjj| dd} t d | |} ttj| gd d djjfS) a= Runs Perceiver Self-Attention, with special (context, latents) appended along the `seq` dimension! Args: context (`tf.Tensor`): Tensor of shape `[bsz, seq, embed_dim]` representing long-form context to resample. latents (`tf.Tensor`): Tensor of shape `[bsz, n_latents, embed_dim]` representing fixed length latents to compress to. Returns: `tf.Tensor`: Tensor of shape `[bsz, n_latents, embed_dim]` representing attention over latents w/ cross from context. r7c s8g|]}tjt||jdjjfgddqS)rrrrperm)r$ transposereshaper0r r ).0x batch_sizer(rr- s*z4TFIdeficsPerceiverAttention.call..z... i d, ... j d -> ... i jT)r8keepdimsz... i j, ... j d -> ... i drQrS)rDrErrHrKr$concatrLrrFrGeinsumrN reduce_maxnnsoftmaxrMrVrUr r ) r(r6r/ seq_lengthr qkvscoresstabilized_scoresr; resampledrrYr-r=s&       &z TFIdeficsPerceiverAttention.call) r>r?r@rAboolrr$rBr=rCrrr+r-r"os"$r"cs@eZdZdeffdd ZdeeejdejfddZ Z S)r#rc sttjd i||jj|_tjjjddd|_tjjj |ddd|_ tjjj dd|_ tjjj |jdd d|_ d S) z:Simple MLP block with intermediate_size and embedding sizerlnrFfcrIactrc_projNr)rrrr r$r%r&r'rkrOrlReLUrmrn)r(intermediate_sizerr)r+rr-rs  zTFIdeficsMLP.__init__ hidden_statesrcCs,||}||}||}||}|S)N)rkrlrmrn)r(rqrrr-r=s    zTFIdeficsMLP.call) r>r?r@rrrtupler$rBr=rCrrr+r-r#s& r#)__doc__typingr tensorflowr$modeling_tf_utilsrconfiguration_ideficsrr%r&Layerrr"r#rrrr-s   ?D